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帅哥 发表于 2008-12-17 19:49:54

NASA－直升机飞行模拟运动需求平台

NASA/TP–1999-208766
Helicopter Flight Simulation Motion Platform
Requirements
Jeffery Allyn Schroeder
July 1999
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NASA/TP–1999-208766
Helicopter Flight Simulation Motion Platform
Requirements
Jeffery Allyn Schroeder
Ames Research Center, Moffett Field, California
July 1999
National Aeronautics and
Space Administration
Ames Research Center
Moffett Field, California 94035-1000
Available from:
NASA Center for AeroSpace Information National Technical Information Service
7121 Standard Drive 5285 Port Royal Road
Hanover, MD 21076-1320 Springfield, VA 22161
(301) 621-0390 (703) 487-4650
iii
Contents
Summary........................................................................................................................................ 1
1. Introduction ................................................................................................................................. 3
Background.............................................................................................................................. 3
Purpose of This Report.............................................................................................................. 10
Approach................................................................................................................................. 10
Contributions........................................................................................................................... 10
Outline ................................................................................................................................... 11
2. The Vertical Motion Simulator........................................................................................................ 13
General Description................................................................................................................... 13
Performance Characteristics......................................................................................................... 13
3. Yaw Experiment........................................................................................................................... 15
Background.............................................................................................................................. 15
Experimental Setup................................................................................................................... 15
Results ................................................................................................................................... 19
4. Vertical Experiment I: Atltitude Control............................................................................................ 31
Background.............................................................................................................................. 31
Experimental Setup................................................................................................................... 31
Results ................................................................................................................................... 34
5. Vertical Experiment II: Compensatory Tracking.................................................................................. 41
Background.............................................................................................................................. 41
Experimental Setup................................................................................................................... 41
Results ................................................................................................................................... 42
6. Vertical Experiment III: Altitude and Altitude-Rate Estimation .............................................................. 49
Background.............................................................................................................................. 40
Experimental Setup................................................................................................................... 49
Results: Objective Performance Data ............................................................................................ 51
7. Roll-Lateral Experiment................................................................................................................. 55
Background.............................................................................................................................. 55
Experimental Setup................................................................................................................... 55
Results ................................................................................................................................... 57
8. Discussion of Overall Results ......................................................................................................... 63
General Discussion.................................................................................................................... 63
Proposed Fidelity Criteria versus Results of Previous Research.......................................................... 63
A General Method for Configuring Motion Systems........................................................................ 66
iv
9. Conclusions................................................................................................................................. 69
Summary ................................................................................................................................ 69
Recommendations for Future Work .............................................................................................. 69
Appendix A—Human Motion Sensing Characteristics............................................................................. 71
Appendix B—Height Regulation Analysis with Previous Model................................................................ 75
Appendix C—Example of Repeated-Measures Analysis............................................................................ 81
Appendix D—Review of Cooper-Harper Handling Qualities Rating Scale.................................................... 83
References....................................................................................................................................... 85
Helicopter Flight Simulation Motion Platform Requirements
JEFFERY ALLYN SCHROEDER
Ames Research Center
Summary
Flight simulators attempt to reproduce actual flight pilotvehicle behavior on the ground reasonably and safely. This
reproduction is especially challenging for helicopter flight
simulators, which is the subject of this work, for the pilot
is often inextricably dependent on external cues for pilotvehicle stabilization. One of the important simulator cues
is platform motion; however, its required fidelity is not
known. Cockpit motion effects on pilot-vehicle
performance, on pilot workload, and on pilot motion
perception were examined in several experiments in order
to determine the required motion fidelity for helicopter
flight simulation. In each experiment, a largedisplacement motion platform was used that, for some
configurations, allowed pilots to fly tasks with a one-toone correspondence between the motion and visual cues.
In all evaluations, representative helicopter math models
were employed, and in two cases a specially developed
model from AH-64 Apache flight test data was used.
Platform motion characteristics were modified to give
motion cues varying from full motion, relative to the
visual scene, to no motion. Four of the six rigid-body
degrees of freedom were explored: roll rotation, yaw
rotation, lateral translation, and vertical translation. The
pitch rotation and longitudinal translation degrees of
freedom remain for future work; however, it was hypothesized that their requirements mirror those of roll rotation
and lateral translation. Several key results were found from
the evaluations. First, lateral and vertical translational
motion platform cues had significant effects on simulation
fidelity. Their presence improved pilot-vehicle
performance, reduced pilot physical and mental workload,
and improved pilot opinion of how faithfully the simulations represented flight. Second, yaw and roll rotational
motion platform cues were not as important as the lateral
and the vertical translational platform cues. In particular,
the yaw rotational motion platform cue did not appear at
all useful in improving performance or reducing workload.
Third, when the lateral translational motion platform cues
were combined with visual yaw rotational cues, pilots
believed they were physically rotating when the motion
platform was not rotating. Thus, an overall efficiency in
the use of motion cues can be obtained by combining
only the lateral translational platform cues with
satisfactory visual cues. Fourth, vertical and roll/lateral
specifications were revised and validated that provide
simulator users with a prediction of motion fidelity based
on the frequency-response characteristics of their motion
control laws. Fifth, vertical platform motion affected pilot
estimates of steady-state altitude during altitude repositionings. This refutes the view that pilots estimate
altitude and altitude rate in simulation solely from visual
cues. Since these studies have shown that translational
motion platform cues had more important effects on
simulation fidelity than did rotational cues, an alternative
to today’s hexapod platform design is suggested which
emphasizes the translational cues. And sixth, the
combined results led to a general method for configuring
helicopter motion systems as well as for developing
simulator tasks that more likely represent actual flight.
The overall results can serve as a guide to future simulator
designers and to today’s operators.
3
1. Introduction
Background
Purpose of Flight Simulation
Flight simulation had its origins near to those of powered
flight itself (ref. 1). Since then, simulation has been used
principally for two distinct disciplines in aviation:
training and research and development. However, flight
training is its most frequent application, in which it is
used primarily to reduce cost and increase safety. Almost
all of the major airlines use flight simulation today
whenever they can receive a training credit for doing so.
This is because an hour in the simulator is less expensive
than an hour in the airplane. For example, a B-747 aircraft
costs about $12,500 per hour to operate versus about
$750 per hour for a 747 simulator (ref. 2).
These cost reductions are put to both training and
retraining uses. The Federal Aviation Administration
(FAA) will certify certain simulators such that, with only
simulator training, a new pilot may fly the actual aircraft
for the first time carrying passengers (ref. 3). Once a pilot
is qualified in a particular aircraft, mandatory periodic
proficiency checks are then conducted in the simulator.
Some of these latter checks may also include recovery
from failures or unusual attitudes (ref. 4). This training is
considered too hazardous to perform in the actual aircraft.
Although the previous discussion relates to fixed-wing
transport training, a similar use for flight simulation is
under way for helicopter training. For helicopters,
however, less is known about what level of fidelity is
needed for these simulators. The FAA has released an
Advisory Circular suggesting fidelity requirements for
helicopter simulators (ref. 5), but little data exist to
support the requirements. Its development started with the
fixed-wing Advisory Circular (ref. 3), and the specifications in most areas were made more stringent owing to
the greater dependency a pilot places on external cues in
helicopter flight than in fixed-wing flight.
The other principal use of flight simulation is for research
and development. When an aircraft system, or component,
reaches a mature level of development, it is often evaluated by a pilot in simulation. These simulations may be
used to evaluate a new vehicle’s handling qualities or the
functionality of a new system component in a more
realistic and safer environment prior to flight testing.
Flight simulation results may also yield a final product,
such as data for a handling-qualities specification. Finally,
flight simulation may be used to determine the causal
factors in an accident. An accident scenario can be
duplicated in order to hypothesize crew action in response
to events.
In the above instances, flight simulation attempts to
imitate flight. Figure 1 illustrates the key components of
simulation and flight. In flight, a pilot receives cues that
indicate vehicle motion in three main ways. First, motion
is perceived from visual cues with the eyes. Second, the
pilot perceives motion from the vehicle’s acceleration.
Third, the pilot can infer, or predict motion, via the
kinesthetic force and position cues that the vehicle’s forcefeel system provides. The latter is an often neglected, but
important, cueing source (refs. 6, 7).
In contrast, the pilot seldom receives any of these cues
accurately in simulation. The aircraft is now represented
by a mathematical aircraft model, which is likely to
contain inaccuracies. The visual system, which is
typically computer generated, does not provide the cueing
richness of the real world. The simulator visual field of
view is usually less than that of the vehicle, and the
visual acuity provided today is incapable of rendering
20/20 vision. The vehicle’s force-feel system is usually
the easiest to replicate, although matching the nonlinear
effects (friction, free-play, and hysteresis) and the inertia
characteristics can be challenging. This challenge results
both from a surprising lack of flight data and from
simulator force-feel system limitations. And because the
simulator displacements are constrained, the motion
system can typically provide only a subset of the in-flight
accelerations. It is the motion system that is the focus of
this report.
Of the above cueing sources, only the motion platform
has practical hard technological limits in its capability to
reproduce the in-flight cues. Thus, in light of those hard
limits and the associated costs of providing them, establishing reliable motion fidelity requirements is warranted.
This is especially true for helicopters, since the pilot often
stabilizes the pilot-vehicle system, and this stabilization
is only possible via feedback from the simulator’s cueing
systems.
The Role of Platform Motion in Flight Simulation
The role of platform motion has been the subject of great
debate. Some researchers and users believe in the extreme
that no platform motion is necessary. Some believe in the
exact opposite. As pointed out by Boldovici (ref. 8),
“Debates about whether to buy motion bases often include
anecdotes, misinterpretation of research results, and
incomplete knowledge of the research issues that underlie
the research results.” Toward understanding the role of
motion in flight simulation, the arguments for the
support of each of these views are given below.
4
Flight
Task
demands
Stick
force
cues
Stick displacement cues
Real-world motion cues
Real-world visual cues
Force-feel
dynamics
Aircraft Pilot
Pilot
Simulation
Task
demands
Simulator
stick
force
cues
Simulator stick
displacement cues
Simulator motion cues
Simulator visual cues
Simulated
force-feel
dynamics
Aircraft
model
Motion
system
Visual
system
Figure 1. Flight versus simulation.
The Case Against Platform Motion. Cardullo
cites many of the reasons users either do not employ
motion or believe it is unnecessary (ref. 9). First,
platform motion usually does not have the face validity
that a simulation component such as the visual system
does. Face validity is defined as a seamless one-to-one
correspondence with the real world. Some subsequently
argue that since the true motion environment cannot be
duplicated faithfully, a subset of it should not be
presented, for the cues are incorrect. It is also argued that
providing platform motion is not cost effective. Finally,
opponents of motion note that transfer-of-training studies
have found no basis for a motion requirement. In
particular, the U.S. Air Force’s
standard view for training is that motion is not required in
the simulation of any aircraft with centerline thrust.
Roscoe (ref. 10) states that “Complex cockpit motion,
whether slightly beneficial or detrimental on
balance . . . has so little effect on training transfer that
its contribution is difficult to measure at all.” The two
studies most often cited showing that motion did not have
a training benefit for several tasks are those by Waters
et al. (ref. 11) and Gray and Fuller (ref. 12). However,
Cardullo points out that these two studies have largely
been discredited because of the poor experimental
apparatus used in each study (ref. 9). In particular, the
motion systems had large motion platform delays.
5
Boldovici (ref. 8), in an extensive review for the U.S.
Army, presents several reasons for not using motion
platforms: (1) the absence of supporting research results,
(2) possible learning of unsafe behavior based on incorrect
platform cueing, (3) achievement of greater training
transfer by means other than motion cueing, (4) undesirable effects of poor motion synchronization, (5) direct,
indirect, and hidden costs, (6) alternatives to motion bases
for producing motion cueing (e.g., g-seats, pressure suits),
and (7) benign force environments.
Poor motion synchronization, which does not have a
precise definition, has caused some pilots to experience
simulator sickness. This discomfort affects both the
pilot’s performance and his acceptance of a simulator.
Surprisingly the discomfort can last, or even develop,
hours after the simulator session. The U.S. Navy has
recommended that motion bases be turned off if sickness
develops; however, that recommendation notes that some
crews also become sick in the actual vehicle (ref. 13).
Some branches of the armed services require a waiting
period between a simulator session and flight.
Finally, several researchers have defined tasks for which
motion does not seem to add benefit. Hunter et al.
(ref. 14) and Puig et al. (ref. 15) indicate that motion
does not seem to be very beneficial for tasks in which the
pilot creates his own motion. Such instances would be for
tracking tasks in a disturbance-free environment.
The Case For Platform Motion. Hall attempts to
determine when platform motion is and is not important
(ref. 16). He contends that non-visual cues are of little
importance for primarily open-loop, low pilot-vehicle
gain, low workload maneuvers with strong visual cues.
However, he also states that motion cues are more
important when the pilot workload increases, when the
pilot-vehicle gain rises, or when the vehicle stability
degrades. The latter certainly occurs in helicopter
simulation.
Showalter and Parris conducted a study in which pilots had
to recover from an engine-out during takeoff in a KC-135
aircraft (ref. 17). They showed that the addition of motion
significantly reduced the amount of yaw activity during an
engine-out when compared to the no-motion case. In the
same study, they also showed that the addition of motion
affected inexperienced pilots’ ability to perform precision
rolling maneuvers, but that the addition of motion had no
significant effect on experienced pilots for the same task.
Young showed that platform motion reduced the pilot’s
response time to a failure or disturbance while on a glide
path compared to the no-motion case (ref. 18). In the same
paper, results were presented showing that when helicopter
pilots and highly trained non-pilots hovered an
unaugmented helicopter model, their performance significantly improved with the addition of motion. Performance
did not improve for the moderately trained non-pilots.
Hosman and van der Vaart (ref. 19) found that performance
improved with motion in roll for both disturbance
rejection and tracking over that of the no-motion case.
However, the roll motion in this case included the
spurious lateral specific force cues owing to the lack of
simulator translational motion available to account for
coordination.
One of the few studies that has examined the performance
effects of full motion versus no motion was performed for
the roll axis by McMillan et al. (ref. 20). That study also
showed significant improvement in tracking, but little
improvement in a transfer-of-training metric when full
motion was present over the no-motion case. Similar
results were present by Levison et al. (ref. 21).
Boldovici (ref. 8), in his balanced presentation on both
sides of the motion argument, gives a set of reasons for
employing motion platforms: (1) to reduce the incidence
of simulator sickness (note that this argument is used
both for and against motion), (2) users’ and buyers’
acceptance of improved validity, (3) trainees’ motivation,
(4) to learn how to perform time-constrained dangerous
tasks, and (5) to overcome the inability to perform some
tasks without motion.
For difficult control tasks, early studies showed that
motion allows a pilot to form the necessary lead compensation more readily with acceleration cues than with the
visual displays alone (refs. 18, 22). For stabilization tasks
a pilot will often use this lead compensation to reduce the
open-loop system phase loss and thus allow an increase in
the pilot-vehicle open-loop crossover frequency to a point
higher than that achieved without motion (ref. 23). This
increased crossover frequency, with the same or better
phase margin, yields tracking performance more akin to
that of flight.
Interestingly, the FAA has been a strong supporter of
platform motion. Indeed, if a device is to be called a
simulator by the FAA, it must have motion. If a device
does not have motion, then the FAA terms it “a flight
training device” (refs. 3, 5).
Developing Requirements for Motion
Since instances have clearly arisen in which the addition
of platform motion shows significant benefits, the
question remains “For those instances, what are the
motion requirements?” Defining the necessary requirements for the quality, or fidelity, of that motion has been
difficult. The fact that requirements are not known is
6
evident from the following quotes from the literature.
“Unfortunately, explicit definitions of ‘valuable’ motion
fidelity, for specific research or training objectives, remain
for the most part undetermined” (ref. 24). “Formal
experiments to determine acceptable attenuation and phase
lag of the force vector are limited in scope . . .”
(ref. 25). “Future research topics in the area of flight
simulation techniques should encompass minimum
essential visual and motion cueing requirements for a
particular flying mission” (ref. 2).
Although definitive answers regarding necessary motion
requirements do not exist, regulators still suggest which
motion degrees of freedom may be useful. These suggestions depend on the level of simulator sophistication
desired by the user. For instance, the FAA specifies two
levels of motion sophistication for helicopter flight
simulators: full six-degrees-of-freedom motion, and threedegrees-of-freedom motion (ref. 5). For the latter, the
nominal three degrees of freedom are pitch, roll, and
vertical. If degrees of freedom different from these are
selected by a user, they must be qualified by the FAA on a
case-by-case basis. Although the selection of the pitch,
roll, and vertical degrees of freedom is reasonable, evidence
to support the selection of these axes or any set of axes is
lacking.
Even though existing motion criteria are incomplete,
however, considerable research has been performed. To
divide and conquer the problem, the six degrees of freedom
are often broken into two categories: rotational motion
and translational motion. But even at this high level,
differences in opinion exist on the relative cueing importance of these two categories. For example Stapleford
et al. (ref. 26) state, for tracking, “Translational motion
cues appear to be generally less important than rotational
ones, although linear motion can be significant in special
situations.” Young states “For most applications, simulation of vehicle angular motions is more important than
translational simulation” (ref. 18). In contrast, the
concluding remarks of Bray (ref. 27) state, “For large
aircraft, due to size and to the basic nature of their
maneuvering dynamics, the cockpit lateral translational
acceleration cues appear to be much more important than
the roll acceleration cues. There was the indication that
this observation might be extended to the generalization
that, in each plane of motion, the linear cues are much
more valuable than the rotational cues.”
Reasons for these differences of opinion at a high level are
unclear and point to the need for additional research. But
before the appropriate directions for the additional research
can be determined, a careful review of past work is
warranted. Those analytical and experimental efforts that
have addressed motion requirements are discussed below.
Analytical Motion Research. Many decisions are
made during both the design and development of a
particular simulation. All of the components shown in
figure 1 must be selected, and their characteristics must be
specified. If an analytical model was available that
accounted for the fidelity effects of these components, then
one could inexpensively make performance trade-offs to
optimize both the cost and utility of a simulator system.
So, a good analytical model would have great use.
Although the dynamics of the non-piloted components of
figure 1 are straightforward, the difficulty facing the
modeler is the pilot block. Pilots are often adaptive,
nonlinear, and inconsistent, and modeling their input/
output characteristics is a challenge. A possible breakdown of the key processes carried out by a pilot is shown
in figure 2. These key processes are sensation, perception,
and compensation. The general characteristics of these
processes are discussed next, because knowledge of them
is relevant to the experimental designs presented in later
sections.
Task
demands
Simulator
stick
force
Simulator
visual, motion,
and stick cues
Remnant
Pilot
Compensation
Perception
Sensation
Figure 2. Top-level pilot model.
The sensation block in figure 2 is often used as the
starting point when motion requirements are hypothesized,
and a large database exists on human motion-sensing
characteristics (refs. 28–36). The details of the human
motion sensing systems are given in appendix A, but four
key points are made here. First, the bandwidth of the total
human motion sensory system encompasses the typical
pilot-vehicle range of frequencies (0.1–10 rad/sec). Second,
for the experiments that are subsequently described, the
thresholds of human motion sensors were exceeded;
however, the literature acknowledges that motion-sensing
thresholds differ among individuals and that they depend
on whether the subject is active or passive during the
7
motion stimulus (ref. 36). Third, previous incorporation
of the motion dynamics and thresholds into models of a
pilot-vehicle system has not resulted in an improved
ability to model pilot-vehicle behavior (ref. 35). Finally,
previous efforts to generate an integrated motion cueing
model have concluded that additional experiments need to
be conducted before that goal can be accomplished
(ref. 31).
Once the cues are sensed, the perception block of figure 2
comes next. At this point, an integrated perceptual process
likely occurs, but how it is accomplished is not known
exactly. One has to know how all of the external cues
(visual, kinesthetic, and tactile) are summed to develop the
pilot’s perception of motion. Unfortunately, little is
known about how these cues affect motion perception, and
further careful experiments are required to explore the
perceptual interactions that occur among these cues.
After the pilot has developed an estimate of the vehicle
state from the output of the perception block, compensation is then applied to this state vector. Fundamentally, it
is known that the pilot applies compensation necessary to
have “integrator-like” or “K/s-like” characteristics in the
crossover region of the pilot-vehicle open-loop combination (ref. 37). To do this, a pilot will typically provide up
to 1 sec of lead before his estimate of a task workload is
degraded.
Application of the above concepts is presented in
appendix B, which gives details of a structural pilot
model for the vertical axis experiment described in
section 4. It is shown that the model captures the general
closed-loop performance trends. However, it underpredicts
the magnitude of these trends to the point that it suggests
no fidelity differences should exist when, in fact, they do
exist. In addition, the model is incomplete, for it does not
account for the gain on motion platform acceleration.
To summarize, a credible analytical model does not yet
exist for flight simulation. More experimental data are
needed to develop and refine the model further. The experiments that have been performed to date are discussed next.
Experimental Motion Research. Many previous
experiments have contributed toward the development of
motion-fidelity requirements. Although some of the data
from these previous studies may be correlated, differences
in visual and motion systems, tasks, and vehicle dynamics
typically prevent the consistent understanding and
development of motion-fidelity criteria. Below, key results
of both rotational and translational experiments are
presented.
Experimental Rotational Criteria. Stapleford
et al. examined the effects of roll and roll-lateral motion
on a pilot’s ability to track a target during a disturbance
(ref. 26). Using both a tracking and a disturbance input,
some key aspects of how the pilot closes the visual and
motion feedback loops were presented. They suggested
that angular cues be accurate in the 0.5–10 rad/sec range;
however, “accurate” was not precisely defined.
Bergeron evaluated the effects of attenuating only the
motion filter gain in the angular degrees of freedom
(ref. 38). For the highly stabilized vehicle that was
simulated, the results suggested that motion has no effect
on the performance of single-axis stabilization tasks.
Motion effects became evident only when simultaneous
control of two angular axes was required. Presenting as
little as 25% of the full motion produced results
comparable to those for full motion.
In the Netherlands, van Gool suggested that second-order
pitch and roll high-pass filters with break frequencies of
0.5 rad/sec appear adequate (ref. 39). This result was for
stabilizing the pitch and roll attitude of a DC-9 on
approach. Both the high-frequency gain and damping ratio
of the motion filter were unity in all of van Gool’s
motion configurations.
Cooper and Howlett examined five tasks with a helicopter
model in an attempt to determine motion fidelity requirements for a particular six-degrees-of-freedom hexapod
motion platform (ref. 40). They made the point that to
achieve maximum results from a simulator, the structure
and values of the high-pass motion filters need to be
tailored for the task while staying within the platform
excursion limits. Although motion amplitude can be
reduced by either reducing the motion filter gain or the
time-constant, their experience had been that it was better
to use the combination of both rather than reducing only
the time-constant. Their tentative conclusion was that it
was best to use a gain of 0.8 in pitch and roll with a timeconstant of 4 sec.
Using a fixed-wing model, the effects of roll-only motion
were examined by Jex et al. (ref. 41). Their recommendation was to provide the pilot with accurate roll-rate
motion cues at frequencies above 0.5–1.0 rad/sec with a
first-order high-pass filter. A filter time-constant of
2–3 sec was recommended. Here, the word “accurate”
included the allowance of a 0.5–0.7 gain on the filter.
Not providing the initial full roll-rate cue was deemed
acceptable.
Shirachi and Shirley used a model of a Boeing 367
transport for a disturbance-rejection task in roll (ref. 42).
The simulator motion platform had sufficient lateral
translational displacement to coordinate the rolling
maneuvers. The results suggested that if the highfrequency gain on the roll high-pass filter was lower than
about 0.5 performance would approach that of no motion.
8
This gain limitation was deemed acceptable with a secondorder high-pass filter break frequency of 0.7 rad/sec.
Bray found that for a large transport aircraft with full roll
gain, motion filter break frequencies of 0.5 rad/sec caused
slight contradictions in the visual and roll motions
(ref. 27). Increasing the break frequency to 1.0 or
1.4 rad/sec resulted in a reduction of some pilots’ ability
to stabilize the Dutch roll motions.
Experimental Translational Criteria. Fewer
experiments have examined translational motion than
rotational motion. Cooper and Howlett (ref. 40) suggested
a lateral translational-axis fidelity criterion, as shown in
figure 3. Second-order filters were used with a hexapod
platform capable of ±5 ft of lateral translation. The
specific points tested to arrive at the best compromise
region were not given.
Motion cues
inadequate
Best compromise
Too much
displacement
1.0
0.8
0.6
0.4
0.2
0.0
10.0 1.0
Motion filter time constant, sec
Motion filter gain
0.1
Figure 3. Suggested lateral translational axis criterion.
Jex et al. expanded their roll study (ref. 41) into the roll
and sway axes (ref. 43), and the effects of the false lateral
translational cue owing to roll attitude were investigated.
A second-order high-pass filter with a damping ratio of 0.7
was inserted into the lateral translational drive path, and a
suggested motion-fidelity criterion of the filter’s gain
versus frequency was proposed as shown in figure 4. A
large region of uncertainty exists because of the limited
range of points tested.
Bray focused on the vertical axis and determined the effects
of motion filter natural frequency on tracking and stabilization tasks with an idealized helicopter model (ref. 24).
He suggested that the vertical acceleration phase-fidelity
should be accurate down to 1.0–1.5 rad/sec. Fidelity was
somewhat arbitrarily defined as the simulation motion cue
not having a phase error of more than 20° relative to the
model. Moderate decreases in pilot-vehicle crossover
frequency and phase margin were noted if the vertical
motion platform gain was lowered from 1.0 to 0.5. No
other gains were examined.
Sinacori Criteria. Sinacori used a six-degrees-offreedom helicopter model (ref. 44). Criteria relating the
motion-drive dynamics to motion fidelity were postulated
from a very limited set of data (four test points); they are
shown in figure 5. The criteria suggest that motion
fidelity can be predicted by examining the gain and phase
shift between the math model and the commanded motion
system accelerations at a particular frequency. The phase
shift between these two accelerations is due to the highpass motion filter placed between the two signals. The
gain and phase of this filter at 1 rad/sec determine the
x and y locations on figure 5, respectively. The amount
by which the commanded motion-system acceleration
phase angle differs from 0° is defined as its phase
distortion. Apparently, a frequency of 1 rad/sec is used,
since that is where the semicircular canals have the
highest gain, as shown in appendix A. The resulting gain
and phase distortions are then located on the appropriate
criterion in figure 5, depending on whether the filter is a
translational or rotational filter.
The criteria show three levels of motion fidelity: high,
medium, and low. The definitions are given at the bottom
of figure 5. As expected, high motion fidelity is associated
with high-gain and low-phase distortion, and low motion
fidelity is associated with low-gain and high-phase
distortion. Sinacori notes that these criteria “. . . have
little or no support other than ‘intuition’” (ref. 44). Still,
these are the most complete criteria proposed to date. The
criteria are either unknown or unused in the simulator
community today, perhaps because they still need to be
validated.
Summary of Criteria. Summarizing the above
criteria, there is apparent agreement that the rotational
gain can be reduced to 0.5 without a fidelity loss, and that
the phase distortion from the high-pass filter should be
minimized at 0.5 rad/sec and above. These requirements
come primarily from studies of roll and of limited pitch.
However, many investigators, somewhat arbitrarily, place
the same requirements on yaw, since they believe the yaw
requirements are natural extensions of the pitch and roll
requirements.
9
Figure 4. Sway motion fidelity criterion (ref. 43).
Low
Specific force
High
Medium Medium
100
80
60
40
20
0
1.0 0.8 0.6
Gain @ 1 rad/sec
0.4 0.2 0.0
Low
Rotational velocity
High
100
80
60
40
20
0
1.0 0.8 0.6
Gain @ 1 rad/sec
Phase distortion @ 1 rad/sec (deg)
0.4 0.2 0.0
High
Medium
Low
Motion sensations are close to those of visual flight
Motion sensation differences are noticeable but not objectionable
Differences are noticeable and objectionable, loss of performance, disorientation
Figure 5. Sinacori motion-fidelity criteria (ref. 44).
10
For the translational motion fidelity, the agreement is less
apparent. The data for the translational requirements are
primarily from lateral translational-axis experiments, with
some data from the vertical axis. There are also disagreements as to whether these translational cues are more
important that the rotational cue, or vice versa.
Since motion fidelity has not been thoroughly examined
in all axes, it is possible that some motion degrees of
freedom are redundant with the other simulator cues that
also allow motion perception. If a motion degree of
freedom is unnecessary, then a savings might be realized
as a result of the reduced complexity in the design,
development, and operation of flight simulators. If a
savings benefit is not chosen by a manufacturer, at least
an operator would know not to concentrate on tuning the
motion in an unnecessary degree of freedom.
An answer to “How much platform motion is enough?” is
likely to be vehicle and task dependent. Vehicles that rely
greatly on the pilot for stabilization, such as helicopters,
will have more stringent platform-motion requirements
than vehicles that do not depend on the pilot for stabilization. As such, this report focuses on the former, more
stringent case.
Purpose of This Report
The purpose of this report is to develop reasonable
guidelines for the use of motion in helicopter simulations.
Areas in which weak guidance exists on how to employ
key motion cues will be strengthened. Specifically, it will
be first determined if yaw requirements are a natural
extension of pitch and roll requirements. Second, the
fidelity effects of vertical motion and their interaction with
visual cues in altitude control will be determined. Finally,
requirements for the relative magnitudes of roll and lateral
translational motion will be investigated.
Approach
Although platform-motion research has been conducted
previously, the approach used here is, perhaps, more
valid. There are several reasons for this greater validity.
First, the world’s largest displacement flight simulator
was used in all of the experiments. Use of this experimental device allows selected flying tasks to be duplicated
faithfully. That is, the math model, the visual cues, and
the motion cues can be matched as a baseline, and then the
effects of altering motion can be subsequently determined.
Second, representative helicopter math models were used,
with one model identified from flight test. This
approach allows helicopter-specific requirements to be
determined. Third, highly experienced test pilots were used
as subjects, and their insightful comments allow confident
extrapolations from simulation to flight. Finally, the
results were corroborated with both objective and subjective data. In most cases, enough data were collected to
allow the measures to be quantified statistically, an
advantage that limited facility-use time often does not
allow.
These methods should provide a high degree of confidence
in the results. They were applied in order to the yaw,
vertical, roll, and lateral translational degrees of freedom.
Motions in the yaw and vertical axes were examined first,
because these motions are the simplest. That is, the
gravity vector remains aligned relative to the cockpit for
these motions. Next, the coupled roll and lateral axes were
explored. These motions are coupled, since the gravity
vector rotates relative to the cockpit. The coupled pitch
and longitudinal axes have been left for future work;
however, the requirements in pitch and longitudinal are
not expected to differ substantially from those of roll and
lateral.
Contributions
1. The results indicate that yaw rotational platform
motion has no significant effect in hovering flight
simulation. For three tasks that broadly represented
hovering flight, the addition of yaw-rotational motion
yielded insignificant changes in pilot-vehicle positioning performance, pilot control activity, pilots’
rating of required control compensation, and pilots’
opinion of motion fidelity.
2. Lateral translation of the motion platform has a
significant effect on hovering flight simulation. For
three tasks that broadly represent hovering flight, the
addition of lateral translational motion improved
pilot-vehicle positioning performance, reduced pilot
control activity, lowered pilot ratings of required
control compensation, and improved pilots’ opinions
of motion fidelity.
3. Lateral translation of the motion platform, plus
typical visual cues, made pilots believe that the
motion platform was rotating when it was not. That
is, pilots believed they were physically rotating when
the yaw platform degree of freedom was stationary. A
hypothesis is that the time delay for the onset of
vection is reduced, thus making pilots believe they
are rotating.
11
4. The three previous contributions may be combined to
suggest that if lateral translational platform motion is
presented, available simulator platform actuator
displacement should not be used for yaw. Instead, the
actuator displacement should be diverted to axes that
can derive more benefit from motion.
5. Vertical platform motion has a significant effect on
simulation fidelity. For target tracking and
disturbance-rejection tasks, the following occurred as
the platform motion neared visual scene motion:
(a) improved tracking and disturbance-rejection
performance, (b) reduced pilot control activity,
(c) improved pilot opinion of motion fidelity,
(d) improved pilot-vehicle target-tracking phase
margin, (e) higher pilot-vehicle disturbance-rejection
crossover frequencies that were correlated with vertical
acceleration phasing rather than with vertical axis
gain, and (f) additional pilot-vehicle disturbancerejection phase margin that was correlated with
vertical axis gain rather than with vertical acceleration
phasing.
6. A previously developed and unvalidated motionfidelity criterion for the vertical axis was revised and
validated. The new criterion predicts the fidelity
effects of changes in the gain and the break frequency
of high-pass motion filters. The revised specification
allows for more reduction in the vertical gain than
previously specified. The revision also suggests that
the combination of a reduction in gain and an increase
in filter natural frequency is worse than either
perturbation alone.
7. Vertical platform motion affected pilot estimates of
steady-state altitude during altitude repositionings.
This result refutes the generally accepted view that a
pilot’s altitude and altitude-rate feedbacks are derived
from the visual cues alone. The implication is that
the input and output cueing assumptions in existing
pilot models are incorrect.
8. For coordination requirements in the coupled roll and
lateral motion axes, a combination of previous and
herein revised criteria resulted in a good prediction of
motion fidelity. Also, substantial improvements in
performance and opinion were demonstrated between
the full-motion and no-motion configurations.
9. A procedure was developed that allows simulator
users to configure their motion platforms to extract
the most simulator fidelity they can from the device.
The procedure uses the fidelity criteria validated
herein. It suggests to users that if they are not
satisfied with their predicted fidelity they should
consider modifying the simulated task.
These results will provide guidance to simulator
manufacturers, operators, and regulators on how to build
better helicopter simulators and how to use them more
effectively for training and research.
Outline
Because the Vertical Motion Simulator at NASA Ames
Research Center was used in all the experiments discussed
in this report, that facility is described first. Five
experiments are then discussed: the Yaw Experiment;
Vertical Experiments I, II, and III; and the Roll-Lateral
Experiment. The experiments are presented in a common
format in which each experiment addresses key questions
that still remain regarding how pilots use platform-motion
cues. The Yaw Experiment focused on the interaction
between lateral translational platform motion and yaw
rotational platform motion. Vertical Experiment I
explored the ways in which the quality of vertical motion
cues affects end-to-end pilot-vehicle performance and pilot
opinion. Vertical Experiment II determined how key
metrics in the pilot-vehicle control loops vary as the
quality of vertical motion cues changes. Vertical
Experiment III examined the interaction between the
vertical platform-motion cue and the visual cues. And in
the Roll-Lateral Experiment, the interaction between roll
and lateral translational platform-motion cues was
explored.
The results of these five experiments are then summarized
and presented as a set of recommendations for the manufacturer and user communities. In the interest of consistency, the recommendations of this work are also discussed
comparatively with the results of previous work in
motion cueing. Finally, a procedure that makes the most
effective use of the recommendations in configuring
motion cues for any motion platform is suggested,
principal conclusions are set forth, and areas requiring
further research are identified.
13
2. The Vertical Motion Simulator
General Description
The Vertical Motion Simulator (VMS) (ref. 45) at NASA
Ames Research Center was used in all of the experiments
reported herein. It is the world’s largest flight simulator.
A cutaway view of the motion system and its position,
velocity, and acceleration limits are shown in figure 6. It
is an electrohydraulic servo system, with a payload of
140,000 lb. This payload is pneumatically counterweighted with pressurized nitrogen. Both the vertical and
lateral translational degrees of freedom are driven with
separate electric motors in a rack-and-pinion arrangement.
The remaining degrees of freedom are hydraulic.
NASA
Ames
VMS Nominal Operational Motion Limits
Axis
Vertical
Longitudinal
Lateral
Roll
Pitch
Yaw
±30
±20
±4
±18
±18
±24
16
8
4
40
40
46
24
16
10
115
115
115
Displ Velocity Accel
All numbers, units ft, deg, sec
Figure 6. Vertical Motion Simulator.
Five interchangeable cockpits are available from which
to choose for each experiment. Each cockpit has a different
window layout and can have a different stick, instruments,
and visual system image generators. Thus, these
characteristics are described separately for each experiment.
Performance Characteristics
The dynamic performance of the VMS depends on the
axis. Using frequency-response testing techniques
(ref. 46), the dynamics of the yaw rotational, longitudinal, lateral, and vertical translational axes were fitted
with equivalent time delays (that is, the phase response
was approximated as a pure time delay) as shown below.
Equations (2) and (3) are taken from previous work
(ref. 47).
..
..
( )
. y
y
sim
com
s
s e »
-0 13
(1)
..
..
( )
.
x
x
s e
sim
com
s
»
-0 17
(2)
..
..
( )
.
y
y
s e
sim
com
s
»
-0 13
(3)
..
..
( )
.
h
h
s e
sim
com
s
»
-0 14
(4)
The subscript sim refers to the actual simulator
acceleration, and the subscript com refers to the
commanded simulator acceleration. Only these four
degrees of freedom are listed because the other two degrees
of freedom were not used in this work.
15
3. Yaw Experiment
Background
Research in the yaw axis has been sparse and
inconclusive. Meiry, in the first detailed investigation into
the effects of yaw rotational motion, found that adding the
motion was beneficial (ref. 22). The study indicated a
reduction in pilot time delay of 100 msec, with a
concomitant improvement in performance. In contrast,
two studies that examined a pilot’s ability to perform
hovering flight tasks with a representative vehicle model
found little or no effect of yaw rotational platform motion
on pilot-vehicle performance or on pilot opinion
(refs. 48, 49).
In the latter studies, the variations in yaw rotational
platform motion ranged from no-motion to full-motion,
where full-motion refers to a platform that moves the
same amount that the math model moves. Pilots were
intentionally located at the vehicle’s center of rotation and
only experienced the rotational motion cues associated
with the vehicle yaw motion. Thus, the translational
accelerations typically produced by yaw motion, when the
pilot is displaced from the center-of-rotation, were absent
by design. The tasks were target tracking in the presence
of an atmospheric disturbance and heading captures. These
results suggested that the usefulness of the yaw degree of
freedom be explored further.
The purpose of the study described in this section is to
extend the previous research and, more specifically, to
determine if yaw platform motion has a significant effect
on pilot-vehicle performance and pilot opinion in situations more representative of flight. If yaw platform
motion is unnecessary, then a savings might be realized
from a reduced level of complexity in the design, development, and operation of flight simulators. In addition, the
actuator displacement usurped by the yaw degree of
freedom could be made available for more useful
displacements in other degrees of freedom.
First, the experimental setup is described, which includes
the three piloting tasks, the vehicle math model, the
simulator cueing systems, and the motion system
configurations that were evaluated. This description is
followed by a presentation of both the objective and
subjective results, which are summarized at the end.
Experimental Setup
There can be confusion when the words “yaw motion” are
used to describe a motion situation. This confusion arises
because the motion that occurs at the vehicle’s center of
mass is different from the motion experienced at the
pilot’s location. For instance, for the purposes here, when
a pilot sits forward of the yaw center of rotation, a vehicle
yawing motion produces both yaw and lateral translation
cues at the pilot’s location. Since the motion at the
pilot’s location is what the simulator is trying to reproduce, all subsequent discussions of motion refer to pilotstation motion. In addition, in this section, the word
“rotation” refers to orientation changes about the yaw
axis, and the word “translation” refers to sway motion in
the vehicle’s y body-axis.
Tasks
Three tasks were developed to represent a broad class of
situations in which both lateral-translational and yawrotational motion cues may be useful in flight simulation.
Task 1 was a small-amplitude command task that allowed
for full math-model motion to be provided by the motion
system. Task 2 was a large-amplitude command task that
did not allow full math-model motion to be presented, for
the simulator cab rotational and longitudinal translational
limits would have been exceeded; however, it was
accompanied by strong rotational visual cues. Task 3 was
a disturbance-rejection task, which also allowed full mathmodel motion to be provided by the motion system, but
with the pilot also controlling vehicle altitude.
Task 1: 15° yaw rotational capture
In the first task, the pilot controlled the vehicle only
about the yaw axis. The pilot was required to acquire
rapidly a north heading from 15° yaw rotational
offsets to either the east or west. This task allowed
for full math-model motion to be represented by the
motion system in all axes (rotational and translational). An aircraft plan view, with the pilot’s
simulated position relative to the inertially fixed
center of mass (c.m.), is shown in figure 7.
The desired pilot-vehicle performance for the task was
to rapidly capture and stay within ±1° about north
with two overshoots or less. This 2° range was
visually demarcated by the sides of a vertical pole,
shown in the pilot’s forward field of view in figure 8.
The pilot’s reference on the aircraft for positioning
was a fixed vertical line centered on the head-up
display. Pilots performed six captures with each
motion configuration, alternating between initial west
and east directions. The repositionings from north to
the initial east or west initial positions were not part
of the task.
16
Figure 7. Pilot location in plan view.
Figure 8. Pilot’s visual scene in Tasks 1 and 3.
Figure 9. Pilot’s visual scene in Task 2.
Task 2: 180° hover turn
The second task required a 180° pedal turn over a
runway, which was to be performed in 10 sec. The
pilot again controlled the aircraft with the pedals
only, and the position of the c.m. remained fixed with
respect to Earth. This maneuver was taken from the
current U.S. Army rotary-wing design standard
(ref. 50) and, with one proviso, is representative of a
handling qualities maneuver performed for the
acceptance of military helicopters. However, in the
military acceptance maneuver, the pilot controls all
six degrees of freedom rather than one.
This maneuver did not allow for full math-model
motion, since the simulator cab cannot rotate 180°.
As a result, attenuated motion was used, as described
later. Desired performance was to stabilize at the end
of the turn to within ±3° and within 10 sec. Pilots
performed six 180° turns, always turning over the
same side of the runway to keep the visual scene
consistent for the set of turns. Figure 9 shows the
visual scene from the starting position.
Task 3: Yaw rotational regulation
The third task required the pilot to perform a rapid 9-ft
climb while attempting to maintain a constant
heading. This disturbance-rejection task was challenging, because collective lever movement in the
unaugmented AH-64 model results in a substantial
yawing moment disturbance (because of engine
torque) that must be countered (rejected) by the pilot
with pedal inputs. This task allowed full vertical,
yaw-rotational, and lateral-translational motion at the
pilot’s station. Desired performance was for the pilot
to acquire the new height as quickly as possible while
keeping the heading within ±1° of north. The same
visual scene was presented as in Task 1 (fig. 8), but
with the scene also indicating height variations as the
vehicle model changed altitude.
Simulated Vehicle Math Model
The math model represented an unaugmented AH-64
Apache helicopter in hover, which had been identified
from flight-test data and subsequently validated by several
AH-64 pilots (ref. 51). Equation (5) describes the vehicle
dynamics for the rotational y and vertical h degrees of
freedom:
17
..
..
.
. . .
. . .
. . .
.
.
. .
. .
. .
y y
d
d
h
z
h
z
r
c
1 1
0 270 0 000 0 000
0 000 0 122 118
0 000 0 000 12 9
0 494 0 266
0 000 14 6
0 000 1 000
é
ë
ê
ê
ê
ê
ù
û
ú
ú
ú
ú
=
-
- -
-
é
ë
ê
ê
ê
ù
û
ú
ú
ú
é
ë
ê
ê
ê
ê
ù
û
ú
ú
ú
ú
+
é
ë
ê
ê
ê
ù
û
ú
ú
ú
é
ë
ê
ê
ù
û
ú
ú
(5)
The collective position dc and pedal position dr are in
inches. The variable z1 was an additional state added to
approximate the effects of dynamic inflow (ref. 51). All
other vehicle states were kinematically related to the above
dynamics. So, in effect, the vehicle c.m. was constrained
to remain on a vertical axis fixed with respect to Earth for
all tasks. Although the tail rotor in an actual helicopter
produces both a side force and a moment about the c.m.,
only the moment was represented in this experiment, a
result of the fixed c.m. These vehicle constraints were
introduced to simplify the number of motion sensations
that had to be interpreted by the pilot. In addition, no
coordination of the gravity vector was required, for it
remained fixed relative to the pilot. No atmospheric
turbulence was present in any of the tasks. The collective
lever was used for Task 3 only.
The pilot was located 4.5 ft forward of the c.m., which
represents the AH-64 pilot location. Thus for this case,
math model rotational accelerations were accompanied by
lateral translational accelerations at the pilot’s station, and
rotational rates were accompanied by longitudinal accelerations at the pilot’s station. Specifically, the accelerations at the pilot’s station in this experiment were as
follows:
axp = -4 5
2
. .
y (6)
ayp = 4 5 . ..
y (7)
.. ..
y y p = (8)
where the subscript p refers to the pilot’s station.
Simulator and Cockpit
The Vertical Motion Simulator, described in section 2,
was used. The mainframe-computer cycle time was 25
msec. The Evans and Sutherland CT5A visual system
provided the visual cues, and it had a math-model-tovisual-image-generation delay of 86 msec (ref. 52). This
delay is typical of today’s flight simulators. The visual
field of view is shown in figure 10. The visual cues
40°
30°
20°
20° 60°
10°
-40°
-50°
-30°
-20°
-20° -40° -60°
-10°
40°
Figure 10. Cockpit visual field of view.
presented to the pilot did not vary and were always those
of the math model. These cues represented the pilot’s
physical offset of 4.5 ft forward of the vehicle’s c.m.
Conventional pedals and a left-hand collective lever were
used. The pedals had a travel of ±2.7 in, a breakout force
of 3.0 lb, a force gradient of 3 lb/in, and a damping ratio
of 0.5. The collective had a travel of ±5 in, no force
gradient, and the friction was adjustable by the pilot.
All cockpit instruments were disabled, which made the
visual scene and motion system cues the only primary
cues available to the pilot. Rotor and transmission noises
were present to mask the motion-system noise. Six
NASA Ames test pilots participated in Task 1, and five of
the same six participated in Tasks 2 and 3. All pilots had
extensive rotorcraft flight and simulation experience.
Motion System Configurations
Four motion-system configurations were examined for
each of three tasks: (1) translational and rotational
motion, (2) translational without rotational motion,
(3) rotational without translational motion, and (4) no
motion. Figure 11 illustrates, in a plan view, the
simulator cab motion for these configurations for Task 1,
which was the ±15° heading turns. In the Translation+
Rotation case, the cab translates and rotates as if it were
placed on the end of a 4.5-ft vector rotating in the
horizontal plane. This case represented physical reality, or
the truth case. In the Translational case, the pilot always
points in the same direction, as the cab translates in x
and y. In the Rotation case, the cab rotates but does not
translate. Finally, in the Motionless case, the cab does not
move.
18
Translational + Rotation Translational
Motionless Rotation
Figure 11. Simulator cockpit motion configurations in plan
view.
When either translational motion or rotational motion was
present for Tasks 1 and 3 (yaw rotational regulation), it
was the full translational or rotational motion calculated
by the vehicle math model. That is, the cockpit provided
the full accelerations that the math model calculated and
that the visual scene provided, along with the effective
motion delays in equations (1)–(4). This statement was
true, except for the longitudinal motion provided by the
translational motion configuration; for yaw turns about a
point, the longitudinal acceleration at the pilot’s station is
always negative (centripetal acceleration in eq. (6)). These
accelerations, if integrated twice to motion-system
position commands, would cause continual longitudinal
cab movement aft for this motion configuration.
Eventually, the simulator cab would exceed its available
longitudinal displacement. Thus, a second order, high-pass
filter was used in the longitudinal axis so that the cab
would return to its initial position in the steady state.
This type filter is typically used in flight simulation, and
it had the form of
..
( )
x
a
s
Ks
s s
com
x m m p
=
+ +
2
2 2
2zw w
(9)
where ..
xcom is the commanded longitudinal acceleration of
the simulator cab,
axp
is the math model’s longitudinal
acceleration at the pilot’s position, K is the motion gain,
z is the damping ratio, and wm is the filter’s natural
frequency.
As described earlier, Task 2 (180° hover turn) did not
allow full motion. Thus, a high-pass filter of the same
form in equation (9) was used in all axes. The values of K
and wm were empirically selected to use as much cockpit
motion as available (fig. 6).
For Task 3, the vertical motion was always the full math
model vertical motion, even in the Motionless condition.
That is, Motionless for Task 3 refers to the simulator cab
being motionless in the horizontal plane. Table 1 lists
K and wm for each tested configuration in each axis.
A configuration with K = 1 and wm = 1.0E-5 rad/sec
effectively makes the filter in equation (9) unity for the
tasks, considering the task time-scale. The filter damping
ratio (z) was 0.7 for all configurations.
Each of these tasks could be performed on a typical
hexapod motion system, except for the vertical translations required in Task 3. In particular, the amount of cab
translation corresponding to the ±15° rotations in Task 1
is less than ±1.2 ft. For Task 2, typical pilot aggressiveness levels resulted in maximum cab yaw orientations of
less than ±5° and lateral travels of less than ±0.5 ft.
Finally, for Task 3, maximum cab yaw orientations were
also less than ±5° and lateral travels were less than
±0.5 ft. However, the vertical simulator cab translation
for Task 3 was near 10 ft, which could not be
accomplished by today’s hexapods.
Procedure
Pilots were asked to rate the overall level of compensation
required for a task using the following descriptors: not-afactor, minimal, moderate, considerable, extensive, and
maximum-tolerable. These descriptors were taken from the
Cooper-Harper Handling Qualities Scale (ref. 53), and
were thus familiar to all the test pilots. For analysis,
these adjectives were given interval numerical values from
–1 to 4, respectively.
Next, the pilots rated the motion fidelity according to the
following three categories: (1) Low Fidelity—motion
cueing differences from actual flight were noticeable and
objectionable, (2) Medium Fidelity—motion cueing
differences from actual flight were perceptible, but not
objectionable, and (3) High Fidelity—motion cues were
close to those of actual flight. These definitions were
taken (and slightly modified) from Sinacori (ref. 44)
(modification discussed later in sec. 4). For this
experiment, pilots had to rely on recollection of their
actual in-flight experience. These subjective ratings were
given numerical values 0 to 2, respectively.
Finally, the pilots were asked to report whether they
perceived any cockpit translational or rotational motion. A
zero was assigned if they did not feel a particular motion.
Each of the four configurations was flown four times in a
random sequence by each pilot.
19
Table 1. Motion filter quantities for yaw tasks.
Rotation Lateral Longitudinal Vertical
Task K wm K wm K wm K wm
(rad/sec) (rad/sec) (rad/sec) (rad/sec)
1 - translational
+rotational
1.00 1.0E-5 1.00 1.0E-5 1.00 1.0E-5 0.00 1.0E-5
1 - translational 0.00 1.0E-5 1.00 1.0E-5 1.00 0.2 0.00 1.0E-5
1 - rotational 1.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5
1 - motionless 0.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5
2 - translational
+rotational
0.35 0.55 0.35 0.55 0.35 0.55 0.00 1.0E-5
2 - translational 0.00 0.55 0.35 0.55 0.35 0.55 0.00 1.0E-5
2 - rotational 0.35 0.55 0.00 0.55 0.00 0.55 0.00 1.0E-5
2 - motionless 0.00 0.55 0.00 0.55 0.00 0.55 0.00 1.0E-5
3 - translational
+rotational
1.00 1.0E-5 1.00 1.0E-5 1.00 1.0E-5 1.00 1.0E-5
3 - translational 0.00 1.0E-5 1.00 1.0E-5 1.00 0.2 1.00 1.0E-5
3 - rotational 1.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5 1.00 1.0E-5
3 - motionless 0.00 1.0E-5 0.00 1.0E-5 0.00 1.0E-5 1.00 1.0E-5
Results
Using standard terminology, the above experimental
design is called a two-factor fully-within-subjects factorial
experiment (ref. 54). The two factors were translational
and rotational motion. Each of the two factors had two
motion levels: present or absent. The combination of the
two levels within each factor results in four motion
configurations for each task. An analysis of variance was
performed on the data taken for each task, with the
observed significance levels (p-values) given below. The
quantity F(x,y) is the estimated ratio of the effects due to
individual subjects, plus the effects due to experimental
variation, all divided by the effects due to individual
subjects. The values of x and y are the numerator and
denominator statistical degrees of freedom, respectively.
The p-values represent the probability of making an error
in stating that a difference exists based on the experimental results when no difference actually exists. If no
difference actually exists, the variations are due to
randomness. Typically, differences are deemed significant
for p < 0.05 (5 chances in 100 of making an error). An
example of how data are processed using the above is
given in appendix C.
Task 1: 15° Yaw Rotational Capture
Objective Performance Data. Figure 12 shows a
representative time-history of several key variables for
Task 1 for both the full motion (Translation+Rotation)
and the motionless condition. Peak yaw rates (not shown)
for the full-motion run were near 10°/sec. Comparing the
full-motion case with the Motionless case shows that the
latter had more yaw rotational overshoots about north,
higher math model yaw rotational accelerations, and larger
control inputs.
Figure 13 depicts, for the four motion conditions, the
means (circles and x’s) and standard deviations (vertical
lines through circles and x’s) of the number of times
pilots overshot the ±1° heading point about north. The
solid and dashed lines connecting the means are drawn to
show trends when going from “No rotation” to
“副瑡瑩潮⸠
20
-20
-10
0
10
20
Yaw rotation, deg
-5
0
5
Ayp
, ft/sec
2
-20
0
20
Yaw rot. accel., deg/sec
2
0 20 40 60 80 100
-2
-1
0
1
2
Pedal, in
Time, sec
Translation+Rotation
-20
-10
0
10
20
Yaw rotation, deg
-5
0
5
Ayp
, ft/sec
2
-20
0
20
Yaw rot. accel., deg/sec
2
0 20 40 60 80 100
-2
-1
0
1
2
Pedal, in
Time, sec
Motionless
Figure 12. Comparison of full motion and no motion for Task 1.
21
No rotation Rotation
0
5
10
15
20
No. of overshoots
No translation
Translation
Figure 13. Measured performance for Task 1.
So, figure 13 shows that when no rotational and no
translational motions were present (Motionless configuration of fig. 11), the mean number of overshoots outside
the ±1° criterion was 11 per run. When only lateral
translational motion was present, the mean number of
overshoots was 7 per run, etc. This measure is generally
indicative of the level of damping, or relative stability, in
the pilot-vehicle system. The analysis of variance for
these results shows that when translational motion was
added, the decrease in the number of overshoots was
statistically significant (F(1,4) = 9.16, p = 0.039). The
decrease in overshoots with the addition of rotational
motion was marginally significant (F(1,4) = 5.58,
p = 0.077). Of the six measures to be discussed, this was
the only task of the three in which the addition of the yaw
platform rotational motion indicated an improvement.
However, the statistical reliability of the improvement
was marginal. The effects of rotational and translational
motion did not interact in this measure (i.e., they were
statistically independent).
Figure 14 illustrates the rms cockpit control (pedal) rate
for the four motion configurations. Often, this measure is
associated with pilot workload, with more control rate
being generally indicative that more pilot lead compensation is required. The analysis of variance for these data
shows that when translational motion was added, the
decrease in pedal rate was statistically significant
(F(1,4) = 18.53, p = 0.013). No significant differences
were noted when rotational motion was added, and
rotational and translational motion effects did not interact.
It is not surprising that the addition of the lateral
translational motion improves the pilot-vehicle performance for this and the later tasks. The addition of the
No rotation Rotation
0
0.5
1
1.5
2
Rms pedal rate, in/sec
No translation
Translation
Figure 14. Control rate for Task 1.
translational cue not only better emulates the real world
cue, but it also provides a strong indication of the
vehicle’s rotational acceleration (eq. (7)). Thus, the pilot
can use this information effectively to place a zero in the
open loop of his rotational-rate control in order to
ameliorate the effects of high-frequency lags.
Subjective Performance Data. Figure 15 shows the
means and standard deviations of the compensation
required (i.e., extensive, considerable, moderate, or
minimum), as rated by the pilots, for the four motion
conditions. When translational motion was added, the
compensation that was required significantly decreased
from considerable to moderate compensation (F(1,5) =
6.83, p = 0.047) and no significant differences were found
for the addition of rotational motion. Rotational and
translational motion did not interact for pilot compensation. These subjective pilot opinions are consistent with
the objective control-rate differences just discussed. That
is, the addition of translational motion reduced control
activity, which in turn likely reduced pilot opinion of the
required compensation.
Similar results were obtained for the pilots’ rating of
motion fidelity, as shown in figure 16. When translational
motion was added, the motion fidelity rating improved
(F(1,5) = 7.74, p = 0.039). The fidelity increased from
low-to-medium to medium-to-high, on average. Although
the data visually suggest an improvement in fidelity with
the addition of rotational motion, the improvement was
not statistically significant. Rotational and translational
motion did not interact in the fidelity ratings.
22
No rotation Rotation
Min.
Mod.
Consid.
Extens.
Compensation
No translation
Translation
Figure 15. Pilot compensation for Task 1.
No rotation Rotation
Low
Med.
High
Fidelity
No translation
Translation
Figure 16. Motion fidelity for Task 1.
Figure 17 depicts whether or not pilots reported lateral
translational motion to be present for the four motion
configurations. Statistically, the two factors of rotational
and translational motion interacted (F(1,5) = 30.6,
p = 0.003). Lateral translational motion was reported an
average of 85% of the time when it was present, and the
addition of rotational motion did not increase lateral
translational motion reports (actually, it decreased lateral
translational motion reports from 91% to 79%). On the
other hand, whereas lateral translational motion was never
reported in the no-motion condition, it was reported nearly
50% of the time when only rotational motion was
present.
No rotation Rotation
0%
100%
% of time trans. mot. reported
No translation
Translation
Figure 17. Lateral translational motion perception for
Task 1.
The influences of rotational cues on lateral translational
motion reports may have been a result of the pilots
sensing some actual lateral translational acceleration in the
rotation-only configuration. The pilot’s design eye-point
was less than 0.5 ft forward of the motion-system’s
rotation point. It is possible that depending on the
variation in pilots’ posture, this small offset may have
resulted in their vestibular system registering a translational acceleration. The maximum rotational accelerations
for the likely worst case (0.5 ft offset and a 20°/sec
2
yaw
accelerations, see fig. 12) results in a 0.005-g translational
acceleration. This acceleration is small but perhaps just
within a pilot’s threshold (see appendix A).
Pilot reporting of rotational motion, shown in figure 18,
was also affected by an interaction between actual
rotational and translational motion (F(1,5) = 10.4,
p = 0.023). Rotational motion was reported 30% of the
time when no motion was present. The reporting of
rotational motion increased dramatically to 87% when any
motion was given. Apparently, when combined with
visual cues, the translational motion enhances the onset of
a phenomenon called vection. Vection is visually induced
motion; that is, it is the belief that one is moving
through a scene when no motion is actually present (a
phenomenon first investigated and reproduced in a laboratory by E. Mach in 1875) (ref. 55). A description of how
the vestibular and visual cues combine to produce vection
has been described by Zacharias and Young (ref. 56).
23
No rotation Rotation
0%
100%
% of time rot. mot. reported
No translation
Translation
Figure 18. Rotational motion perception for Task 1.
To summarize the results for this task, translational
motion was clearly the most important motion variable.
Translational motion improved pilot-vehicle performance,
lowered control activity, lowered pilot compensation,
improved pilot impression of motion fidelity, and caused
pilots to believe that rotational motion was present when
it was not. The addition of rotational motion showed no
statistically significant improvement, with the exception
of a marginal statistically significant decrease in the
number of overshoots.
Task 2: 180° Hover Turn
Objective Performance Data. Figure 19 is a
representative time-history of several key variables for the
Translation+Rotation motion and Motionless conditions
in Task 2. Peak math model, and thus visual, yaw rates
for this turn were 50°/sec (not shown). These rates were,
of course, attenuated by the motion system (table 1) so
that it remained within its displacement constraints. In the
Motionless configuration, an increase in yaw overshoots
is noted, which is evident in the displacements, rates,
accelerations, and control inputs. These trends are
consistent with those of Task 1.
Figure 20 depicts, for the four motion conditions, the
means and standard deviations of the number of times
pilots overshot the ±3° heading criterion about the runway
centerline during the 180° turns. When translational
motion was added, the decrease in the number of overshoots was marginally significant statistically (F(1,4) =
5.40, p = 0.081). Interestingly, in this case, the addition
of rotational motion made the performance worse, and this
result was statistically significant (F(1,4) = 13.26,
p = 0.022). Rotational and translational motion did not
interact in this measure.
These results are not easily explained; however, it must be
remembered that in this task the motion platform never
presented the pilots with full math-model motion. It is
therefore possible that some false cueing in rotation,
owing to the motion filter and its selected parameters, had
a negative effect on performance in this case. A rough
approximation confirms this possibility. For instance, if
one modeled the yaw rotation between 0° and 180° by
y
p
w =
2
sin t (10)
then the peak yaw rate would be (p/2)w. Since the peak
yaw rates were 50°/sec, this gives a natural frequency of
approximately 0.6 rad/sec. This frequency is a reasonable
approximation of the heading time-histories, if one
discounts the holding times at both 0° and 180°. That is,
the periods would be about 10 sec. Since the yaw rotational motion filter for this configuration had a break
frequency of 0.55 rad/sec, the motion cue’s phase distortion at the task frequency was 90°. It is possible that this
distortion was adversely affecting performance.
Figure 21 illustrates the rms pedal rate for this task. The
analysis of variance indicated that the decrease in pedal rate
was statistically significant when translational motion
was added (F(1,4) = 11.69, p = 0.027). No significant
differences were noted when rotational motion was added,
and translational and rotational motion effects did not
interact.
Subjective Performance Data. The average pilotrated compensation required for this task is shown in
figure 22. Large variations in pilot opinion occurred, but
no statistically significant differences were noted. Based on
the variation in the data, one cannot say that the motion
configurations affected the amount of subjective compensation required. However, the trends shown in figure 22
follow those in Task 1 (fig. 15).
Figure 23 shows the mean motion-fidelity ratings for
Task 2. Here motion fidelity was significantly higher
when translational motion was present (F(1,4) = 47.9,
p = 0.002), whereas the presence of rotational motion did
not affect rated fidelity. Rotational and translational effects
did not interact. So, pilots believed that the lack of translational motion was objectionable as compared to flight.
However, the lack of rotational motion, as long as there
was translational motion, was not perceived as a fidelity
degradation.
24
-150
-100
-50
0
50
100
Yaw rotation, deg
-5
0
5
Ayp
, ft/sec
2
-50
0
50
Yaw rot. accel., deg/sec
2
0 20 40 60 80
-2
-1
0
1
2
Pedal, in
Time, sec
-150
-100
-50
0
50
100
Yaw rotation, deg
-5
5
0
Ayp
, ft/sec
2
-50
0
50
Yaw rot. accel., deg/sec
2
0 20 40 60 80
-2
-1
0
1
2
Pedal, in
Time, sec
Figure 19. Comparison of full motion and no motion for Task 2.
25
No rotation Rotation
-2
0
2
4
6
8
10
No. of overshoots
No translation
Translation
Figure 20. Measured performance for Task 2.
No rotation Rotation
0
0.5
1
1.5
2
Rms pedal rate, in/sec
No translation
Translation
Figure 21. Control rate for Task 2.
No rotation Rotation
Min.
Mod.
Consid.
Extens.
Compensation
No translation
Translation
Figure 22. Pilot compensation for Task 2.
No rotation Rotation
Low
Med.
High
Fidelity
No translation
Translation
Figure 23. Motion fidelity for Task 2.
Figure 24 illustrates pilot reports of whether lateral
translational motion was present. There were significantly
more reports of lateral translational motion when it was
present (70%), than when it was not (5%) (F(1,4) = 14.8,
p = 0.018). There was no significant effect of rotational
motion on lateral translational motion reports, nor was
there any significant rotational motion and lateral
translational motion interaction (unlike Task 1).
In the reporting of rotational motion, the rotational and
translational motion factors interacted (fig. 25) (F(1,4) =
20.0, p = 0.011). Rotational motion was reported 90% of
the time when translational motion was present, both
when rotational motion was actually present and when it
was absent. Only when translational motion was absent
did the presence of rotational motion lead to increased
reports of rotational motion. When no motion was
presented, rotational reports occurred 27% of the time on
average, but increased to 65% of the time when a
rotational motion was added.
To summarize the results for this task, lateral translational
motion was again the key motion variable. Its addition
reduced control activity, improved motion fidelity, and led
to the belief that rotational motion was also present when
it was not present. These results are similar to those of
Task 1, except for the interesting result that the addition
of rotational motion degraded performance slightly in
Task 2.
26
No rotation Rotation
0%
100%
% of time trans. mot. reported
No translation
Translation
Figure 24. Lateral translational motion perception for
Task 2.
No rotation Rotation
0%
100%
% of time rot. mot. reported
No translation
Translation
Figure 25. Rotational motion perception for Task 2.
Task 3: Yaw Rotational Regulation
Objective Performance Data. Figure 26 depicts key
variables in a sample run for the Translation+Rotation and
Motionless conditions in Task 3. The peak yaw rate for
this run was 7.5°/sec (not shown). The peak yaw
accelerations for this task were similar to those of Task 1,
but the rms accelerations were slightly higher in Task 3
than in Task 1 (5.67°/sec
2
versus 4.21°/sec
2
, respectively).
The amount of visual rotation was less in this
disturbance-rejection task than in the command task of
Task 1. Slightly more acceleration overshoots were
present when motion was removed.
Figure 27 depicts, for the four motion conditions, the
means and standard deviations of the number of times
pilots had an excursion outside ±1° about north per run.
When translational motion was added, the decrease in
the number of overshoots was statistically significant
(F(1,4) = 8.06, p = 0.047). The addition of rotational
motion did not yield a significant difference. The effects of
rotational and translational motion did not interact in this
measure.
Figure 28 illustrates the pedal rate for the four
configurations. Unlike the results for the previous two
tasks, the addition of translational motion did not significantly reduce the rms pedal rate. However, the addition of
rotational motion actually increased pedal rate (F(1,4) =
18.74, p = 0.012). The rotational and translational motion
effects did not interact. So this is another case in which
the addition of rotational motion made matters worse;
however, as figure 28 shows, the percentage increase in
control rate was not dramatic.
The cause for the performance degradation with the
addition of rotational platform motion is unknown. The
opposite result occurred in Task 1. No attempts were made
to explain why with an analytical model; that is left for
future work. However, during the model development, a
modeler will face the difficulty of determining how a pilot
integrates both the rotational and translational cue.
Subjective Performance Data. Average pilot
compensation ratings are shown in figure 29. The
improvement in the ratings for the translational motion
conditions, relative to those in the rotational motion
conditions, was marginally significant (F(1,4) = 6.38,
p = 0.065). The addition of rotational motion resulted in
no statistical difference in compensation. The effects of
rotational and translational motion did not interact.
The same result occurred for rated fidelity, which is
presented in figure 30. The addition of translational
motion resulted in an improvement in fidelity ratings that
was marginally significant (F(1,4) = 6.15, p = 0.068).
The addition of rotational motion again made no difference. The effects of rotational and translational motion
were statistically independent.
Figure 31 illustrates the percentage of the time that pilots
reported the presence of lateral translational motion. The
addition of translational motion significantly increased the
number of reports of lateral translational motion (F(1,4) =
12.1, p = 0.025). Interestingly, the addition of rotational
motion led to a marginally significant decrease in the
reports of lateral translational motion (F(1,4) = 5.4,
p = 0.08).
Figure 32 illustrates the percentage of the time that pilots
reported the presence of rotational motion. No significant
effects were found, with rotation being reported an average
of 73% of the time, independent of the motion
configuration.
27
-10
-5
0
5
10
Yaw rotation, deg
-5
0
5
Ayp
, ft/sec
2
-20
0
20
Yaw rot. accel., deg/sec
2
0 50 100 150
-2
-1
0
1
2
Pedal, in
Time, sec
-10
-5
0
5
10
Yaw rotation, deg
-5
0
5
Ayp
, ft/sec
2
-20
0
20
Yaw rot. accel., deg/sec
2
0 50 100 150
-2
-1
0
1
2
Pedal, in
Time, sec
Figure 26. Comparison of full motion and no motion for Task 3.
28
No rotation Rotation
0
10
20
30
40
50
No. of overshoots
No translation
Translation
Figure 27. Measured performance for Task 3.
No rotation Rotation
0
0.5
1
1.5
Rms pedal rate, in/sec
No translation
Translation
Figure 28. Control rate for Task 3.
No rotation Rotation
Min.
Mod.
Consid.
Extens.
Compensation
No translation
Translation
Figure 29. Pilot compensation for Task 3.
No rotation Rotation
Low
Med.
High
Fidelity
No translation
Translation
Figure 30. Motion fidelity for Task 3.
No rotation Rotation
0%
100%
% of time trans. mot. reported
No translation
Translation
Figure 31. Lateral translational motion perception for
Task 3.
No rotation Rotation
0%
100%
% of time rot. mot. reported
No translation
Translation
Figure 32. Rotational motion perception for Task 3.
29
In summary, the results of this task do not reveal many
differences from those of Tasks 1 or 2. Lateral translational motion was again the dominant variable. Its
addition improved pilot-vehicle performance, although its
effect on rated pilot compensation and fidelity was less
reliable than it was in the other two tasks. The only
statistically significant effect of rotation was a deleterious
effect on control activity.
Combined Results
Table 2 summarizes the effects of the presence of lateral
translational or yaw rotational motion on the six measurables for the three tasks. All of the improvements in the
measures occur for the presence of lateral translational
motion, except for one. In that case, a marginally
significant effect was noted in pilot-vehicle stability for
the addition of yaw rotation. Statistically significant
degradations in the measures occurred only for the addition
of yaw rotational motion. Interactions between lateral
translational and yaw rotational motion occurred only in
the lateral translational and yaw rotational motion
perception reporting. However, in Tasks 1, 2, and 3,
pilots reported (on average) rotational motion being
present 87%, 90%, and 80% of the time, respectively,
when only lateral translational motion was present.
The claim made as a result of these evaluations is that
yaw rotational platform motion is not adding value to
helicopter flight simulation. However, the claim that yaw
rotational platform motion does not provide a cue is not
made. During the course of these studies, a hypothesis
was raised that the pilots simply were not sensing
whatever cue the yaw rotational platform motion was
providing. As previously stated, each task resulted in
enough motion so that the physiological thresholds, as
measured in passive situations, were exceeded. However,
the possibility remained that, since the pilots were
actively a part of the control loop rather than passive
observers, those passive thresholds may have risen to the
point that the yaw rotational motion platform cue was not
being sensed.
This hypothesis is rejected based on the following. To
determine if whatever cue the yaw rotational platform
motion might be providing was indeed being sensed, an
additional motion configuration was evaluated on an ad
hoc basis. That configuration simply reversed the sign in
the yaw rotational platform motion command. So for
math model rotations to the right, the visual scene
correspondingly moved to the right, but the platform
moved a symmetric amount to the left. This configuration
was extremely disliked by all the pilots, some of whom
experienced physical discomfort. Thus, what appears to be
happening in the typical motion configuration (with
correct motion signs) is that the yaw rotational platform
motion cue is simply confirming the already compelling
rotational cues that come from the visual scene. This
conclusion is also supported by the earlier studies
performed by the author (refs. 48, 49) in which the pilot
was sitting at the rotational center. In that case, no
translational cue was present, so the yaw rotational cue
was redundant with the visual cue.
Table 2. Summary of yaw task results.
Translational Rotational
Translational/
rotational interaction
Task: 1 2 3 1 2 3 1 2 3
Measure
Pilot-vehicle stability + + + + - o
Control rate + + o o o -
Compensation + o + o o o
Fidelity + + + o o o
Translational reporting + + o - x
Rotational reporting o o x x
+: Significant improvement; +: marginal improvement; -: significant degradation; o: no effect; x: interaction.
30
In addition to these compelling visual cues, when lateral
translational motion is present, its combination with the
visual cues provides more than enough cues to cause the
pilot to believe he is rotating when he is not physically
rotating. When the yaw rotational platform cue is in the
opposite direction, it is no longer providing confirmation,
but it is instead providing conflict. Consistent with this
explanation is the dictum “bad motion is worse than no
motion” in that spurious motion cues destroy any vection
that has been generated by the other motion and visual
cues (ref. 9).
Earlier it was stated that Meiry’s yaw experiment (ref. 22)
showed that the addition of rotational motion improved
performance. It is possible that the difference in experimental setup between that experiment and the one reported
here could account for the difference, although the results
of Task 1 in the present study did agree with those of
Meiry by showing a marginal improvement in performance. In Meiry’s experiment, pilots countered a considerable yaw rotational disturbance (white noise with a
15° rms), did not control a model representative of a
helicopter (the model resulted in yaw rate proportional to
pilot input at all frequencies), and the visual system was a
line on an oscilloscope. So, with this deprived-cue visual
system and with only a yaw rotational cue, yaw rotational
motion might have helped. However, Meiry’s study did
not determine if the lateral translational motion cue could
have provided an equivalent substitute for the yaw
rotational cue, which was the principal result shown here.
Effect of Results
The effect of these results is twofold. First, for simulators
with independent-axis motion drives, that is, dedicated
servos for each axis, excluding the yaw platform rotational
degree-of-freedom capability would result in a cost savings
to manufacturers. To users of motion simulators that
already have a yaw rotational degree of freedom, less time,
if any, needs to be spent configuring and tuning that axis
for a given application.
Second, for simulators without independent-axis motion
drives, such as the common synergistic hexapod motion
systems (fig. 33), not using the yaw platform rotational
degree of freedom allows for more available displacement
in the other motion axes. Since the same set of actuators
is used to move the platform to a desired position or
orientation, the available displacement or orientation of
any axis is a function of the displacement or orientation in
another axis.
An example of the dependency is shown in figure 34,
which was generated by Cooper and Howlett (ref. 40).
Loss of available motion in the longitudinal, lateral, and
vertical axes also occurs for rotations about the yaw axis.
Thus, users should disable yaw rotation and thereby gain
additional benefits in axes that provide added value in
flight simulation.
Figure 33. Typical hexapod motion drive system.
60
y = 0°
y = 20°
y = 40°
y = 60°
–60
–60 0
Pitch attitude, deg
Roll attitude, deg
60
0
Figure 34. Effect of yaw angle orientation on pitch and roll
angles.
31
4. Vertical Experiment I:
Altitude Control
Background
For helicopters, only one study has focused extensively on
the vertical axis (ref. 24). In that study, the effects of the
motion-filter natural frequency were examined, but almost
always with a high-frequency gain of unity. The research
described here also examined motion-filter natural
frequency, but in addition evaluated the combined effects
of motion-filter gain.
The setup of the experiment is described, including a task
description, the math model, and the simulator cueing
systems. This description is followed by the results,
which subsequently validate a revised motion-fidelity
criterion for the vertical axis.
Experimental Setup
Task
The vertical task required the pilot to increase aircraft
altitude 10 ft. To do so, the pilot used visual cues to place
the horizon between two red squares on an object 50 ft
away, as shown in figure 35. These sighting objects were
used previously, in both flight and simulation, for vehicle
model validation (ref. 57). The red region had a height of
0.75 ft, which was the final altitude tolerance.
0.75 ft
2.25 ft
10 ft
Bob-ups
Red
Horizon
Figure 35. Sighting object for vertical tracking.
An altitude displacement, or bob-up, of 10 ft was chosen
for two reasons. First, 10-ft bob-ups could be performed
in the VMS without any attenuation of the math model
accelerations. This situation is referred to as 1:1 motion,
since the simulator cockpit motion is the same motion as
that calculated by the vehicle model, and the motion is
also the same as that shown by the visual scene. Second,
when the aircraft was 10 ft below the target, the target
remained in the field of view of the visual scene.
The desired performance standard for this task required that
the pilot make only two or fewer reversals outside the red
region before stabilizing in the red. Adequate performance
required two or fewer reversals outside the top and bottom
horizontal boundaries of the objects, which are 2.25 ft
apart.
The bob-up task was to be performed as fast as possible.
Five bob-ups were completed for each motion-system
configuration. Pilots repositioned the helicopter downward
to the starting position between each bob-up. This
repositioning was performed at the pilot’s discretion
without performance standards. Thus, the bob-down was
not part of the evaluation.
Simulated Vehicle Math Model
The vertical-axis dynamics were selected by averaging the
hovering characteristics (at sea level) of five helicopters:
the OH-6A, BO-105, AH-1G, UH-1H, and the CH-53D.
Heffley et al. (ref. 58) was the source for these dynamics.
The resulting vertical-acceleration-to-collective-position
dynamics were as follows:
..
( )
.
h
s
s
s
c d
=
+
9
0 3
(11)
A delay is usually added to a model such as this to
approximate the lag caused by the rotor dynamics, but this
delay was instead subsumed by the simulator motion and
visual systems. This technique was successfully used in a
previous model validation experiment (ref. 59). In
addition, no torque or rpm limits were imposed on the
pilot. Also, since the task was limited to a single axis,
there was no coupling into the directional axis. Atmospheric conditions were calm air, no turbulence, and
unrestricted visibility. The math model cycle time was
25 msec.
Simulator and Cockpit
The NASA Ames VMS, described in section 2, was again
employed. For this experiment, only the vertical degree of
freedom was used. A Singer Link DIG-I image generator
provided the visual scene, which was representative of the
32
state of the art in the late 1970s. This visual image
generator was different from that reported in section 3, a
result of scheduling constraints at the VMS facility. The
image generator that was used did not have a texturing
capability. The visual delay from the math model to the
visual image was 83 msec (ref. 52), which is a typical
delay for flight simulator image systems. Visual lead
compensation was not used to reduce the visual delay, the
purpose being to more closely match the visual and
motion delays. This matching is imperfect, since the
vertical-axis frequency response can be approximated by an
equivalent time delay of 140 msec (eq. (4)). Thus, in this
experiment, the visual response effectively leads the
motion response.
The visual field of view was presented on three windows
that spanned ±78° horizontally and +12° and –17° vertically as shown in figure 36. The center window had the
principal objects in the field of view for the task, and the
information presented in the left and right windows was
limited to polygonal color variations on the ground. The
image that the pilot viewed for the task is shown in
figure 37.
Figure 36. Cockpit field of view for vertical tracking.
Figure 37. Pilot’s visual scene for vertical tracking.
A conventional left-hand collective lever was used. It had a
travel of ±5 in, had no force gradient, and the friction was
adjustable by the pilot. All flight instruments were
disabled so that all cues were from the visual scene and the
motion system. Rotor and transmission noises were
present to partially mask the motion-system noise. The
head-up display shown in figure 37 was stowed for the
task. Three NASA Ames test pilots participated; they are
hereinafter referred to as pilots A, B, and C. All three fly
rotorcraft and had extensive simulation experience.
Motion System Configurations
A second-order high-pass filter, typical of that used in
most flight simulators, was placed between the math
model vertical acceleration and the simulator-commanded
acceleration. It had the form of
..
..
( )
h
h
s
Ks
s s
com
=
+ +
2
2 2
2zw w
(12)
where ..
hcom is the commanded vertical acceleration of the
simulator cab, ..
h is the math model’s vertical acceleration
at the pilot’s station, K is the motion gain, z is the
damping ratio, and w is the filter’s natural frequency.
The damping ratio of the above filter was held fixed at
0.7. The two parameters that are used to then keep the
motion system within its displacement limits for a given
math model and task are K and w. To reduce simulator
motions, either K is reduced or w is increased. Achieving
the proper balance between these two possible ways of
reducing the motion, while trying to minimize a loss
in motion fidelity, is not well defined. The criterion
suggested by Sinacori and discussed in section 1 was used
to select the characteristics of the motion configurations.
To validate or modify the criterion, 10 sets of gains and
natural frequencies were chosen to span the criterion, as
shown in figure 38. The values for the filter are given in
table 3. Note that configurations V3 and V4, from
table 3, result in gain and phase-distortion coordinates of
and , respectively. Although
these filters have unity gain at high frequencies (K = 1),
the dynamics of the high-pass filter cause the above
attenuations and phase distortions at 1 rad/sec, which is
the frequency used to plot the motion filters against the
criterion.
33
Figure 38. Motion configurations for vertical tracking.
Table 3. Motion-filter quantities for vertical tracking.
Vertical
Configuration K w
(rad/sec)
V1 1.000 0.010
V2 0.901 0.245
V3 1.000 0.521
V4 1.000 0.885
V5 0.650 0.245
V6 0.670 0.521
V7 0.300 0.245
V8 0.309 0.521
V9 0.377 0.885
V10 0.000 —
Procedure
For the pilot to rate the motion fidelity of each
configuration, a baseline was established for comparison.
The baseline specified that the pilots perform the task first
with 1:1 motion; this was a calibration run. Here, the
pilots were told that they had 1:1 motion, and that the
sensations they were feeling were to be interpreted as the
“actual aircraft.” The motion fidelity of all future configurations was then compared to this 1:1 motion baseline
configuration. This testing procedure allowed immediate
back-to-back comparisons of the effects of motion
parameter changes. This procedure mitigates some of the
problems that occur in simulation fidelity experiments
that compare the actual aircraft to the simulation back-toback, even when the events occur in the same day.
34
During the evaluations, the pilots had no knowledge of
the configuration changes, except when they were told of
the full-motion “calibration runs.” Still, the 1:1 motion
case (configuration V1 in table 3) was also evaluated;
during its evaluation, the pilot was not told he had the
1:1 configuration.
Pilots A and C completed all the configurations listed in
table 3 in one session, Pilot B completed all the configurations in two sessions, and all of the configurations
were evaluated by all three pilots. Some configurations
were repeated if time permitted. For each configuration,
pilots assigned a motion-fidelity rating using the definitions in figure 4. Then, the pilots answered questions
regarding the aircraft’s characteristics, their task performance, and the compensation required to perform the task.
The pilots were also asked to estimate the relative use of
the motion and visual feedback cues.
Results
The results of Vertical Experiment I consist of objective
performance data and subjective fidelity ratings. Relevant
pilot comments will be added in the discussion of these
results.
Objective Performance Data
Again, compelling performance differences are shown
between full motion and no motion in figure 39
(configurations V1 and V10, respectively). For the
configuration V1 case, well damped, accurate bob-ups
were achieved with the vertical velocity remaining within
10 ft/sec; the vertical acceleration remained within 0.5 g,
and the collective stayed within 1.5 in. A drastic difference
is evident in the no-motion case (V10). Initially, the pilot
overshot the 85-ft desired altitude and then returned to the
starting point as he responded to the unfamiliar cues.
Since the acceleration feedback cue was the only cue that
changed from the full- to the no-motion case, part of the
pilot’s collective input must have been a result of that
cue. With the acceleration cue removed in the V10
configuration, the pilot had to adjust his compensation
based on the new set of cues. With time in the motionless
configuration, the performance improved, but even the
final repositioning took longer in the fixed-base case than
in the full-motion case. These differences have obvious
training implications, for the pilot must develop different
mental compensation between the two cases. No data were
taken when returning to the full-motion case for recalibration, but pilots commented that relearning the full-motion
configuration was easy and natural.
Figures 40 through 49 show phase-plane portraits for each
motion configuration. Each portrait shows three runs, one
for each pilot, using his best performance run if an evaluation was repeated. Both the bob-up and bob-down are
shown. These plots show how the motion changes
affected the pilot’s ability to capture the upper desired
point smoothly. Ideally, these plots should be welldamped and approximately oval.
Figure 40 indicates that some overshoots were present for
the full-motion condition, but overall the trajectories were
well damped and smooth. There was reasonably precise
control about the 85-ft altitude point, with one instance of
a pilot not arresting the vertical velocity soon enough,
which caused a position overshoot. Little change in
performance, or perhaps a slight improvement, is noted
for the slight motion changes of configuration V2 in
figure 41.
The high-gain, moderate phase-distortion case of
configuration V3 in figure 42 still produces well-damped
trajectories on ascents. The acceleration cues for this
configuration lead the math model by 90° at 0.52 rad/sec.
If the motion between the ascents and descents were sinusoidal, the frequency of the maneuver would be approximately 2 rad/sec. This value is approximated by letting
h t = + 80 5sin w (13)
. cos h t = 5w w (14)
. / sec / sec
max
h ft rad = &THORN; = 10 2 w (15)
For the initial phase of the maneuver, therefore, the
motion cues would be accurate, but the subsequent
stabilization at a particular altitude would produce some
miscues, for lower frequency content would be present.
Pilots seemed to have more difficulty in returning to the
starting point than in the first two configurations;
however, this repositioning was not officially part of the
evaluations. For the high-gain and high-phase-distortion
case of configuration V4 in figure 43, the overshoots were
more prominent, and the overall control was much less
precise.
35
70
75
80
85
90
Altitude, ft
-15
-10
-5
0
5
10
Vert. vel., ft/sec
-30
-20
-10
0
10
20
Vert. accn., ft/sec
2
0 50 100
-3
-2
-1
0
1
2
Collective, in
Time, sec
70
75
80
85
90
Altitude, ft
-15
-10
-5
0
5
10
Vert. vel., ft/sec
-30
-20
-10
0
10
20
Vert. accn., ft/sec
2
0 50 100
-3
-2
-1
0
1
2
Collective, in
Time, sec
Figure 39. Altitude control: full motion versus no motion.
36
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 40. Phase-plane portrait for configuration V1.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 41. Phase plane portrait for configuration V2.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 42. Phase-plane portrait for configuration V3.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 43. Phase-plane portrait for configuration V4.
For the medium-gain, low-phase-distortion case of
configuration V5 in figure 44, the bob-up trajectories
show good performance, with the trajectories being
similar to those of the full-motion case, or at least similar
to those of the V2 case. The gain is lowered to 65% of
full motion for the V5 configuration.
Increasing the phase distortion from configuration V5 to
configuration V6 caused a slight reduction in the precision
around the desired stationkeeping point with a few undershoots of the ascent point as shown in figure 45. The gain
reduction in going from V3 to V6 does not appear to
degrade performance significantly.
The low-gain, low-phase-distortion case, configuration V7
in figure 46, resulted in less precision with more over- and
undershoots than in the previous configurations. Here the
gain is at 30% of full motion at high frequencies.
Performance definitely decreases when the gain drops from
0.65 in configuration V5 to 0.3 in V7.
The precision in V7 also appears worse than in the V4
case of high gain and high phase distortion. Increasing the
phase distortion to the V8 case in figure 47 shows a
further reduction in precision from that of the V7 case.
Again, the gain of 30% is insufficient, but the combination of the low gain with some phase distortion makes the
performance even more degraded.
The low-gain, high-washout case of configuration V9 in
figure 48 exhibits a further degradation, the middle of the
desired oval being occupied with the aircraft’s trajectory.
Finally, the no-motion case of configuration V10 in
figure 49 indicates the worst performance of all; the
imprecise control of velocity is obvious at both the top
and bottom points.
37
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 44. Phase-plane portrait for configuration V5.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 45. Phase-plane portrait for configuration V6.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 46. Phase-plane portrait for configuration V7.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 47. Phase-plane portrait for configuration V8.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 48. Phase-plane portrait for configuration V9.
-20 -10 0 10 20
70
75
80
85
90
Vertical velocity, ft/sec
Altitude, ft
Figure 49. Phase-plane portrait for configuration V10.
38
Subjective Performance Data
Figure 50 shows pilot motion-fidelity ratings along with
the postulated boundaries of Sinacori (ref. 44). The pilots
unanimously rated the high-gain, low-washout configurations V1 and V2 as having high fidelity. If either the gain
was reduced (as in V5) or the phase distortion increased
(as in V3), one of the three pilots reduced his rating to
medium. When a combination of this gain and phase
distortion was examined, that is, configuration V6, the
ratings dropped on average across one level. However, one
of the pilots did not perceive differences between the V3 or
V5 configuration and the V6 combination.
- Pilot A
- Pilot B
- Pilot C
M M
V4
100
80
60
40
20
0
1.0 0.8 0.6 0.4
Gain @ 1 rad/sec
Phase distortion @ 1 rad/sec (deg)
0.2 0.0
M
L L
V9
L
L M
V8
L L M
V6
M H
H
H
V3
M
M M
V7
L H H
V5
M H H
V2
H
H H
H
V1
H
L L
L
V10
Fixed base
Low
Medium
High
L
H M
Figure 50. Motion fidelity ratings for altitude control.
The next individual changes in gain or phase from the V3
and V5 cases to the V4 and V7 cases produced, on average,
a decrease from high to medium fidelity. However, a
combination of these effects, encompassed by the V9
configuration, produced unanimously low fidelity ratings.
Unsurprisingly, the fixed-base configuration, V10, also
produced unanimously low fidelity ratings.
These ratings suggest that for this task, the fidelity
criterion for the vertical axis appears to decrease from
high-gain/low-phase-distortion in a direction toward the
fixed-base case. The combination configurations V2,
V6, and V9 result in ratings predicted by the criterion.
However, either reducing the gain or increasing the phase
distortion resulted in fidelity ratings better than predicted,
on average.
Combined Results
It is reasonable to suggest a revision to the Sinacori
criterion (ref. 44) if objective performance, subjective
pilot fidelity ratings, and subjective pilot comments are
consistent. Each of the configurations is discussed, and
when a consistency exists that does not align with the
criterion, then a modification to the criterion is suggested.
With the full-motion case, configuration V1, pilots were
consistent in their comments that they achieved desired
performance, that compensation was minimal to moderate,
and that they felt that they could be very aggressive with
the vehicle. All ratings were high fidelity, and the
objective performance was good.
For the configuration with a slight decrease in gain and an
increase in phase distortion, V2, the comments indicated
that a difference was noted. Two of the three pilots noticed
a slight decrease in performance, and the compensation
increased over that of the V1 case. Yet all pilots rated the
configuration high fidelity, since they felt is was “close to
visual flight.” Here is where the fidelity definitions could
be made more precise, because a pilot is faced with a
potential dilemma with a configuration that has noticeable
but not objectionable differences (medium fidelity definition), yet is close to visual flight (high fidelity definition). Pilots apparently felt that when it was necessary to
group the ratings into three categories, the V2 configuration was in the most favorable category. Yet they all
perceived a difference. It appears that the pilots may have
been sensitive to the differences in the immediate acceleration because of collective input. This initial acceleration is proportional to the high-frequency motion gain,
and it is reasonable that the 10% change between these
configurations could be noticed. It is also reasonable that
the variation was not objectionable.
For the high-gain and moderate phase-distortion case,
configuration V3, all pilots noticed the necessity to
change their control input technique. Two pilots noticed
the unpredictability, or the “slipperiness,” of this configuration. Only one downgraded the fidelity rating to
medium. The objective performance did not change
appreciably in the bob-up, but difficulty was perceived in
repositioning downward. Although the ratings indicated
the configuration was high fidelity, the comments and the
performance did not agree. Thus, the V3 configuration
should not be placed in the high-fidelity region.
Increasing the phase distortion further to the V4
configuration resulted in a perceived degradation in
39
performance, and this is evident from the phase-plane data.
The aggressiveness level was reduced, and the required
compensation increased. Two of the three pilots noticed a
phasing difference between the visual and the motion cues,
yet with all of the above, they rated this configuration no
worse than medium. However, one pilot thought that it
was close to being objectionable. Considering the
comments and the performance, it appears that the fidelity
was closer to low than the rated medium, and thus no
change is suggested.
Comparing these near-unity motion gain results with the
only other set of high quality experimental data for helicopters (ref. 24) shows similar trends. That experiment
showed that for tracking a randomly moving target, while
the open-loop crossover frequency remained nearly
invariant with increases in motion-system phase
distortion, the phase margin increased by 30° when going
from a motion phase distortion of 108° to a phase distortion of 16°. For a phase distortion of 43°, which is similar
to configuration V3, a phase margin degradation of 15°
was measured. The comments and objective performance
data from the present experiment, for these cases, indicated
a similar trend in stability degradation, yet the subjective
fidelity ratings for this task still rated the V4
configuration as medium fidelity.
In another experiment (ref. 60), for aircraft with poor
longitudinal flying qualities in a landing task, it was
observed that phase distortions higher than 90° caused
“essentially uncontrolled touchdowns.” Thus, considering
previous data and the lack of consistency in this experiment’s objective and subjective results for these configurations, it seems appropriate to retain the hypothesized
fidelity breakpoints suggested by Sinacori (ref. 44) for the
near-unity gain configurations.
Returning to the low phase-distortion configuration with
moderate gain, V5, pilot comments and objective performance generally indicated no degradation from the fullmotion case. Only one pilot rated the fidelity as medium
(owing to a slight vertical oscillation). From the consistency of these results, it appears that when the phase
distortion is less than 30°, lowering of the gain required
for high motion fidelity from 0.8 to 0.6 is appropriate.
Keeping essentially the same motion gain, but increasing
the phase distortion to configuration V6 resulted in
comments indicating a performance loss because of a
reduction in precision and aggressiveness. Fidelity ratings
of medium are consistent with Sinacori’s hypothesized
boundaries (ref. 44).
A reduction of gain to 0.3, but with a low phase
distortion in configuration V7 resulted in adequate
performance, fears of overshooting, and comments that the
motion was almost unusable. These comments were
repeated for the remaining configurations, V8 and V9; the
objective performance worsened as the phase distortion
increased. From the comments and ratings, configurations
V7, V8, and V9 were consistent with Sinacori’s hypothesized boundaries (ref. 44). Therefore, regardless of the
phase distortion, a gain of 0.3 results in low fidelity.
Referring to the fixed-base configuration, V10, two pilots
said that they put the control input in the wrong direction
during the run. Pilots were generally stunned at the effects
of the total loss of motion. During the 1:1 calibration
runs, one pilot made a comment prior to the evaluations
that “The visual scene is so compelling at conveying the
error that it seems to primarily be a visual task . . . I
would expect the effects of motion to be minor.” Another
pilot commented after the fixed-base configuration “I have
never experienced such a dramatic disconnect from reality
as during that configuration, as compared to the fullmotion case.” Thus, caution should be used when interpreting a priori conjectures on the value of motion, even
when given by experienced test pilots with considerable
simulation experience.
Motion cues were certainly perceived by the pilots in all
but this fixed-based configuration. This is evident from
the performance and comments, but it is also consistent
with tests that have determined the vertical-acceleration
sensing threshold to be in the 0.09-0.27 ft/sec
2
range
(ref. 19). This threshold was exceeded in all but the fixedbase configuration.
Suggested Revision to Fidelity Criteria
Based on these results, a revision to the vertical axis of
Sinacori’s criterion is suggested in figure 51. Here, the
changes consist of lowering the gain required when the
phase distortion is low. The gain for the high-fidelity
region is reduced to account for the results of the V5
configuration. The medium-fidelity region has been
extended to a lower gain, but kept above that of the V7
configuration, which has a gain of 0.3. These suggestions
are consistent in trend with the data of Mitchell and Hart
(ref. 47), which indicated a preference for low-gain, low
phase-distortion motion over high-gain, high phasedistortion motion. Also, the fidelity boundaries have been
rounded to account for the reduction in fidelity when a
gain attenuation is combined with phase distortion. The
determination of exactly where the rounding should begin
and end requires additional data.
Also, slight wording changes are suggested to the motionfidelity definitions of figure 4 to alleviate the previously
mentioned difficulties in their use. The word “disorientation” should be removed; it caused some pilots to shy
40
away from the low rating. Although a configuration may
have been objectionable, the pilots felt no disorientation.
The suggested rewording, as noted on figure 51, should
suffice, since any disorientation experienced would be
expected to receive an “objectionable” description.
Significant differences in measured performance and in
perceived fidelity were evident across these configurations.
The analytical model discussed in appendix B predicted a
performance decrease when the motion filter natural
frequency increased. However, the predicted bandwidths of
the primary loop did not differ by a factor of 1.5, which
was the general guideline suggested for determining
dissimilarity between motion configuration responses
(ref. 61). Although this analytical model is perhaps the
most reasonable available, this experiment demonstrated
that additional data are needed in order to continue its
refinement and validation.
Medium
Low
Ref. 44
Modified
Vertical axis
High
80
60
40
20
0
1.0 0.8 0.6
Gain @ 1 rad/sec
Phase distortion @ 1 rad/sec (deg)
0.4 0.2 0.0
High
Medium
Low
Motion sensations are not noticeably different from those of visual flight
Motion sensation differences are noticeable but not objectionable
Motion sensation differences are noticeable and objectionable
Figure 51. Suggested vertical criterion and fidelity definitions.
41
5. Vertical Experiment II:
Compensatory Tracking
Background
In Vertical Experiment I, the global performance effects of
motion-filter gain and natural frequency variations were
examined during tracking. In Vertical Experiment II, a
more detailed examination of the same filter variations
was conducted for a new task: key pilot-vehicle frequencyresponse metrics were measured during combined tracking
and disturbance regulation. This procedure allowed for the
influence of the motion-filter changes to be examined
simultaneously for these two important piloting tasks.
The task and experimental apparatus are first described.
Then, objective pilot-vehicle performance metrics and
subjective motion-fidelity ratings are discussed.
Experimental Setup
Task
Figure 52 shows the display presented to the pilot. The
object was to null the error between the moving target
aircraft and the horizontal dashed line that was fixed to the
pilot’s aircraft.
A system block diagram depicting how the error developed
is shown in figure 53. Two external inputs were used in a
scheme similar to that developed by Stapleford et al.
(ref. 26).
Target Aircraft
Fixed horizontal line
e
Figure 52. Pilot’s display for vertical compensatory
tracking.
i
h dc
dc
d
dc
tot
h
filt
+
+
+
Aircraft
–
e
h
sim
h h
Pilot
h
dc tot
(s)
1
s
2
Motion
hardware
Motion
filter
Visual
hardware
Figure 53. Vertical compensatory loop.
The target was driven by a sum-of-sines (SOS) input, and
the vehicle was disturbed by a separate SOS input that
was summed with the pilot’s collective position. These
SOS’s were as follows:
i t t t
t t
t t
t
h( ) . sin( . ) . sin( . )
. sin( . ) . sin( . )
. sin( . ) . sin( . )
. sin( . )
= +
+ +
+ +
+
2 573 0 15 2 202 0 34
1 563 0 64 0 923 1 13
0 411 2 05 0 150 3 56
0 040 6 32 feet
(16)
dcd t t t
t t
t t
t
( ) . sin( . ) . sin( . )
. sin( . ) . sin( . )
. sin( . ) . sin( . )
. sin( . )
= +
+ +
+ +
+
0 029 0 28 0 058 0 49
0 999 0 80 0 167 1 50
0 209 2 67 0 201 4 63
0 148 8 50 inches of collective
(17)
Each component of each SOS completed an integral
number of cycles in the task time-length of 204.8 sec.
A warm-up period of 10 sec preceded the run, and a cooldown period of 3 sec followed the run. To prevent the
pilot from separating target motion from disturbance
motion, the disturbance input, dcd, was selected so that its
resulting altitude spectral content (when filtered by the
vehicle dynamics) matched the target shaping function
(refs. 26, 41). If the pilot is able to separate the target
motion (which is sensed only visually) from the disturbance motion (which is sensed visually and vestibularly),
previous research has shown that pilots may alter their
behavior and potentially ignore the motion cues when
nulling the target motion (ref. 26). The above spectral
matching is an attempt to prevent this behavior.
The above shaping function was determined empirically.
The compromised result of this shaping was that the
highest frequency component of the target input was
below the simulator’s visible threshold of 3–4 arc min,
and the lowest component of the disturbance input was
below the vestibular translational acceleration detection
threshold of 0.01 g’s (ref. 28). As shown in figure 53,
the pilot received two external cues for use in zeroing the
target error, e: a visual cue, and a motion cue. The
dynamics between the pilot input and these cues are
discussed in the sections that follow; however, only the
block labeled “motion filter” was modified in this experiment. The details of these blocks will be described later.
Although the pilot was instructed to null the displayed
error constantly, the desired performance for the task was
to keep the error within one-half the height of the target
vertical tail for half of the run length. The target was
placed 100 ft in front of the aircraft, and the height of the
vertical tail was 3 ft.
42
Simulated Vehicle Math Model
The vertical-axis dynamics were the same as for Vertical
Experiment I given by equation (11). Again, only this
single degree of freedom was modeled, and the pilot
controlled this degree of freedom with a collective lever in
the cockpit.
Simulator and Cockpit
The simulator and cockpit were also the same as for
Vertical Experiment I. All flight instruments were again
disabled. Six NASA Ames test pilots participated (three of
the six being the same three who participated in Vertical
Experiment I), hereinafter referred to as pilots A–F. All
pilots had extensive rotorcraft flight and simulation
experience.
Motion System Configurations
The second-order high-pass motion filter given by
equation (12) was used. The gains, damping ratio, and
natural frequencies evaluated were the same as for Vertical
Experiment I, which are given in table 3.
Procedure
All configurations were tested in blind evaluations, and
they were randomized. Pilots were asked to rate the
motion fidelity of each configuration, using the motionfidelity definitions given in figure 4. Between each
configuration, in order to calibrate or recalibrate themselves to the true vehicle model response, pilots flew the
model with full motion (configuration V1) in a visual
scene depicting objects of known size (shown in fig. 35).
All six pilots flew all 10 configurations.
Results
Objective Performance Data
Time-histories and standard deviations of several pertinent
variables for a full-motion case (V1) and a no-motion case
(V10) are shown in figure 54. Both of these runs were
made by the same pilot. This comparison reveals that
when going from full motion to no motion, the target
error, vehicle acceleration, and collective displacement all
increase. Rather than compare time-histories across the
10 cases and six pilots, several pilot-vehicle performance
metrics were determined, and the statistical significance
was evaluated.
First, since the two sum-of-sines inputs were statistically
independent, two effective open-loop pilot-vehicle
describing functions may be determined (ref. 41). One
open-loop describing function applies to the target errors
caused by target motion; it is determined by calculating
the ratios of the Fourier coefficients of h(jw)/e(jw) at the
Figure 54. Compensatory tracking: full motion versus no motion.
43
target input frequencies. The other describing function
applies to target errors caused by the disturbance input; it
is determined from the ratios of the Fourier coefficients of
–dc(jw)/ dctot(jw) at the disturbance input frequencies.
These two describing functions are referred to the “target
following” and “disturbance rejection” describing functions
hereinafter.
From these describing functions, open-loop crossover
frequencies and phase margins were determined by linear
interpolation. Figure 55 shows the magnitude and phase
responses of the disturbance-rejection describing function.
The data for this example are from the full-motion run
(V1) in figure 54. Linear interpolation between the data at
the appropriate frequencies (shown in fig. 55) gives a
crossover frequency of 3.3 rad/sec and a phase margin
of 28°.
The above two measures provide useful information about
the character of the pilot-vehicle response. In particular,
the crossover frequency is a rough measure of how fast
the error is initially zeroed; the higher the crossover
frequency, the faster the initial nulling of the error. The
phase margin is a rough measure of the damping ratio of
the error response; the higher the phase margin, the more
damped the error response.
Each of these open-loop describing functions includes a
different combination of both the pilot’s internal visual
and motion compensation applied to the visual error and
to the acceleration feedback (refs. 26, 41). Although only
the motion cues were changed here, the pilot’s internal
compensation may change in an attempt to account for
degradations in either the visual or motion cues. The
overall effect of these changes on pilot-vehicle
performance and opinion are given next.
Target Following. The target-following crossover
frequencies are given in figure 56 for all of the configurations. For easy reference, the motion-filter gain K and
natural frequency w, rounded to one decimal place, are
indicated above each bar. The mean value (bar height) and
standard deviation (horizontal line above bar) are shown,
each determined from six values. Each of the six values
corresponds to the individual average for each pilot across
his runs. Upon examining the variance ratio, or F-test
10
-1
10
0
10
1
-20
-10
0
10
20
Magnitude, dB
Phase margin
10
-1
10
0
10
1
-300
-250
-200
-150
-100
Phase, deg
Frequency, rad/sec
Crossover frequency
{
Figure 55. Example disturbance-rejection describing function.
44
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
0
1
2
3
4
Configuration
Crossover freq., mean and rms, rad/sec
1.0
0.0
0.9
0.2
1.0
0.5
1.0
0.9
0.7
0.2
0.7
0.5
0.3
0.2 0.3
0.5
0.4
0.9
0.0
0.0
K
w
Figure 56.Target-following pilot-vehicle open-loop crossover frequencies.
(ref. 54), for these data, the differences among the configurations were not significant at the 5% level. In addition,
no coherent trend was present among the motion-filter
variations. This result agrees with, and extends, the results
of Bray (ref. 24), which indicated an invariance in targetfollowing open-loop crossover frequency for motion filter
natural frequencies between 0.2 and 1.25 rad/sec with K =
1 for all configurations. The results from the present
experiment indicate that this invariance across natural
frequency variations also holds for motion-gain changes.
Thus, it appears that the initial quickness with which the
pilot closes the target-tracking loop does not depend on
the motion cue. That result is intuitive; it might be
expected that the speed with which this loop is closed
would be based on a pilot’s mental model of the speed
with which the loop should be closed. The pilot initiates
and generates his own motion in this loop.
Figure 57 shows the target-following loop phase margins
for all configurations. Unlike the target-following crossover results, statistical differences are present in the phase
margins, although the range of the means is only 10°. The
largest phase margin occurred with the full-motion
condition (V1), and phase margin was progressively lost
as K was reduced and wm was increased. Using the
Newman-Keuls method (ref. 62) to determine which
means are statistically different from each other at the 5%
level, the results indicate that configurations V1 and V3
were different from V4 and from V6–V10, and that
configurations V2 and V5 were different from V8.
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
0
10
20
30
40
50
Configuration
Phase margins, mean and rms, deg
1.0
0.0 0.9
0.2
1.0
0.5
1.0
0.9
0.7
0.2
0.7
0.5
0.3
0.2 0.3
0.5
0.4
0.9
0.0
0.0
K
w
Figure 57. Target-following pilot-vehicle open-loop phase margins.
45
These results are partially consistent with those of Bray
(ref. 24), which indicated that target-tracking phase margin
degraded as wm was increased from 0.2 to 1.25 rad/sec at a
unity gain (K = 1). However, Bray’s results exhibited
larger variations in the measured phase margins, including
an almost 20° variation between wm = 0.2 and 0.5 rad/sec.
Only a slight difference was measured in this experiment
between the nearly equivalent V1 and V2 motion
configurations.
These phase-margin target-following results suggest that
the resulting damping of the error to a nulled steady-state
value is affected by motion variations. This result is
consistent with the results in section 4. That is, as the
quality of the motion improved in that purely targetfollowing task; the principal effect on performance was in
damping (see figs. 40–49).
It was also suggested in section 4 that the high-fidelity
portion of the Sinacori criterion should include configuration V5. The performance results shown here for target
following are consistent with that suggestion.
Disturbance Rejection. Pilot-vehicle open-loop
crossover frequencies for the disturbance-rejection loop are
shown in figure 58. The crossover magnitudes appear to
be roughly ordered by phase distortion level, that is, by
successive increases in wm. The statistical results reveal
that at the 5% level, configurations V1, V2, and V7 all
had higher crossover frequencies than the fixed-base case
V10. Again, these results are consistent with the
configurations tested by Bray (ref. 24), and they extend
those results by suggesting that the open-loop crossover
appears to be affected by changes in wm at all levels of
motion-filter steady-state gain K.
Figure 59 shows the disturbance-rejection loop phase
margins. Here the configurations are ordered by progressive reductions in K. Statistically, configuration V3 was
better than any of the other cases, and configurations V1
and V4 were better than V10. Bray showed, for K = 1, that
the low and high phase distortion cases have roughly the
same phase margin, with perhaps a slight peaking at
moderate amounts of phase distortion. Here, more relative
peaking in phase margin was observed for the V3 case
than was found by Bray. The crossover frequency for
V3 was reduced, which alone might contribute to an
increased phase margin; however, V4 had a low crossover
frequency, but not an accompanying phase-margin peak.
With the unknown details of the pilot feedbacks, the V3
configuration must be coupling in with the vehicle
dynamics in a manner different from that in the other
configurations.
The overall disturbance-rejection results suggest three
points. First, the speed at which the disturbance is rejected
is affected primarily by the high-pass motion filter’s
natural frequency. Second, motion-filter gain appears to
affect the relative damping of the disturbance-rejection
loop, rather than being driven more by filter natural
frequency as in the target-following loop. Third, both the
lowest crossover frequency and the lowest phase margin of
the disturbance-rejection loop occurred in the no-motion
case.
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
0
1
2
3
4
Configuration
Crossover freq., mean and rms, rad/sec
1.0
0.0
0.9
0.2
1.0
0.5
1.0
0.9
0.7
0.2 0.7
0.5
0.3
0.2
0.3
0.5 0.4
0.9
0.0
0.0
K
w
Figure 58. Disturbance-rejection pilot-vehicle open-loop crossover frequencies.
46
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
0
10
20
30
40
50
Configuration
Phase margins, mean and rms, deg
1.0
0.0
0.9
0.2
1.0
0.5
1.0
0.9
0.7
0.2
0.7
0.5
0.3
0.2
0.3
0.5
0.4
0.9
0.0
0.0
K
w
Figure 59. Disturbance-rejection pilot-vehicle open-loop phase margins.
Total Tracking Error. Vertical tracking errors are
shown in figure 60. This error accrues from both the
target and the disturbance inputs. The four lowest errors
occurred for the lowest phase-error configurations, but the
differences between any two of the motion configurations
were not large. The Newman-Keuls results indicated that
all of the motion configurations, V1–V9, had better
performance than the no-motion V10, while no trackingerror differences were present among the V1–V9 configurations at the 5% level. Hence, the biggest effect on error
reduction was simply the presence of motion rather than
its characteristics.
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
0.0
0.5
1.0
1.5
2.0
2.5
Configuration
Tracking error, mean and rms, rad/sec
1.0
0.0 0.9
0.2
1.0
0.5
1.0
0.9
0.7
0.2
0.7
0.5
0.3
0.2
0.3
0.5
0.4
0.9
0.0
0.0
K
w
Figure 60. Vertical tracking errors.
47
Subjective Performance Data
Figure 61 provides the motion-fidelity ratings using the
definitions stated earlier. The ratings are divided into
“high,” “medium,” “low,” and “split,” where split refers to
a pilot assigning inconsistent ratings for repeated runs of
the same configuration. Split ratings only occurred for the
V1 configuration; this was more likely, because
configuration VI had more repeat evaluations than the
other cases. The ratings indicate that no pilot perceived the
K = 0.3 cases to be high fidelity. Configuration V2
received the best overall ratings, and also had the lowest
mean tracking error as shown earlier. Configuration V3
surprisingly received two low ratings; V4 received no low
ratings. Configuration V6, which is essentially a combination of the natural frequency of V3 and the gain of V5,
was rated worse than either V3 or V5. No configurations
in which the high-pass filter’s high-frequency gain was
less than 0.3 was judged to be high fidelity. All pilots
rated the fixed-base condition to be low fidelity.
Summarizing the results of this section, the presence and
quality of motion influenced both target tracking and
disturbance rejection. Motion-filter natural frequency
affected both target tracking and disturbance rejection,
whereas motion gain affected only disturbance rejection.
Overall, the results from this dual task are consistent with
those presented in section 4.
Low
High
Medium
High, Medium, Low, Split
Fixed base
0,0,6,0 V10
80
60
40
20
0
1.0 0.8 0.6
Gain @ 1 rad/sec
0.4 0.2 0.0
Phase Distortion @ 1 rad/sec (deg)
0,5,1,0
V9
0,2,4,0
V8
1,5,0,0
V6 V3
3,3,0,0
V4
3,1,2,0
0,2,4,0
V7
2,4,0,0
V5 V2
5,1,0,0
V1
3,1,0,2
Figure 61. Pilot motion fidelity ratings for vertical task.
49
6. Vertical Experiment III: Altitude
and Altitude-Rate Estimation
Background
Relative to flight, simulation has perennially produced
less effective control of altitude and altitude rate. Fixedwing landings in simulation usually have higher runway
position dispersions and higher mean touchdown velocities than in flight. Bray showed that with a 16-in blackand-white television monitor, simulator vertical
displacements of at least ±20 ft are required in order to
achieve the desired fidelity in the landing task (ref. 60).
In helicopter simulations, pilots often comment that the
vertical damping (as perceived from the visual and motion
cues) is less than it is in flight (refs. 59, 63–65). Since
the math models have often been developed and validated
from in-flight measurements, attempts to determine the
cause have focused on the simulator visual and motion
cues.
Often, the assumption is made that pilots obtain vehicle
acceleration information from the motion system, and rate
or position information from the visual system, as shown
in figure 62. Thus, decreasing motion gain or increasing
washout frequency is assumed not to affect the outer rate
and position loops. In this work, an experiment was
designed to test this assumption by exploring the effects
of visual scene properties and vertical motion on the
estimation of altitude and altitude rate.
Experimental Setup
Five factors were incorporated in Vertical Experiment III:
(1) reposition direction, (2) initial altitude, (3) presence of
vertical motion, (4) visual scene level-of-detail control,
and (5) visual scene mean object size.
Tasks
The experiment involved two tasks.
1. In the first task, the altitude repositioning task, pilots
were instructed either to double or halve their initial
altitude. The initial altitudes were 15.6, 18.3, and
21.0 ft. Altitude was the only degree of freedom under
the pilot’s control. No time requirement was placed
on the task, and no requirements were imposed on
overshooting or undershooting what the pilots felt to
be their doubled (or halved) altitude.
2. In the second task, the altitude-rate control task,
pilots were instructed to climb or descend at a
constant rate of 3 ft/sec. The climbs started at an
altitude of 7.5 ft and ended at 42.5 ft. The descents
started at an altitude of 42.5 ft and ended at 7.5 ft.
These unusually specific altitudes were selected to
allow one-to-one motion and visual cues in the
Vertical Motion Simulator.
For both tasks, all instruments were disabled so that
pilots would have to estimate their altitude and altitude
rate from the visual scene, motion system, or cockpit
collective movement. Although pilots would ordinarily
accomplish these tasks in flight by referring to an
instrument that measures either altitude or altitude rate,
these instruments were intentionally disabled so that the
possible value of the motion and visual cueing variations
could be determined.
Pilot
Acceleration
cue
Rate-of-climb and
altitude cues
Aircraft
model
Motion
system
Visual
system
Figure 62. Typical assumptions in the apportioning of
simulation cues.
Simulated Vehicle Math Model
The math model represented the AH-64 Apache helicopter:
..
.
.
. .
.
.
.
h
z
h
z
c
1 1
0 122 118
0 0 12 9
14 6
1 00
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
=
- -
-
é
&euml;
ê
ù
&ucirc;
ú
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
+
é
&euml;
ê
ù
&ucirc;
ú[ ]
d (18)
The state z1 was added to approximate the effects of
dynamic inflow into the rotor. All other displacements and
orientations were held fixed at zero.
Simulator and Cockpit
The simulator and cockpit were the same as for the yaw
experiment described in section 3. The Evans and
Sutherland CT5A visual system was used, but both the
hardware and software were modified to achieve the visual
variations described below.
50
Design of Visual Scenes
Simulated scenes are typically less dense (fewer objects
per degree of field of view) at lower altitudes than at
higher altitudes. In flight, objects invisible at high
altitudes become visible at low altitudes, as the angle that
the object subtends at the eye becomes larger. When this
subtended angle exceeds an optical resolution threshold,
the object can be seen. These changes in detail cannot be
represented accurately owing to the required computational
load in the image generator. Instead, visual databases,
which are sets of objects, are stored and then faded in and
out of the scene according to range. This fading is adjusted
so that the transitions between databases appear natural.
How an image generator accomplishes this fading of
objects is called “level-of-detail management.”
For this experiment, visual databases were constructed of
green polygons on a brown ground plane. These polygons
were randomly positioned at locations within a 430-ft
radius forward of the aircraft. Each database was associated
with a particular altitude band (2.5-ft band). These bands
occurred between altitudes of 5 and 45 ft. As the vehicle
moved between these 2.5-ft altitude bands, one database
smoothly faded out while the new database faded in.
For each altitude, whether or not a polygon was displayed
at that altitude depended on which of two visual thresholds
was used. The “small” visual threshold allowed objects to
be drawn when their vertical visual angle spanned at least
0.1°; the “large” visual threshold allowed objects to be
drawn when their vertical visual angles spanned 0.2°.
In either case, the polygons were all rendered in a common
set. That is, the polygons were all distributed on a ground
plane; whether or not they were then drawn depended on
the visual threshold used. Thus, at the 0.1° threshold,
more polygons were drawn than in the 0.2° threshold. The
polygons that would appear in the 0.2° threshold would
also appear at the 0.1° threshold. Four different ground
planes of polygons, two each for the two visual
thresholds, were used so that any effects found would not
be due to some unfortuitous and unusual random
placement of the polygons.
Three levels of “level-of-detail management” were
evaluated: high, medium, and low. For the high condition,
62 polygon sizes were distributed exponentially in the
database with diameters between 0.025 ft and 17.23 ft.
The exponential spread was determined according to the
relation
d I
i
i
= = 17 23 0 9 0 1 61 . ( . ) , ,..., (19)
where di is the diameter of the ith polygon.
These 62 polygon sizes were placed in the common
database in the following manner. First, the largest
polygons were randomly placed until a specified portion of
the unfilled area was filled. Then, polygons of the next
largest size were randomly placed in the remaining area
until that same proportion of the unfilled area was filled.
This continued, allowing no overlapping regions, until
polygons of all 62 sizes were placed. Since the image
generator could display a maximum of 1350 polygons at
60 Hz, the proportion parameter was selected such that
this maximum was not exceeded.
For the medium condition, the polygon sizes were
distributed evenly, not exponentially, between 0.025 ft
and 17.23 ft. For the low condition, only a single database
was presented at all altitudes, so no fading in and out
occurred. For the low condition, two sets of only three
polygon sizes were used: one set had polygon diameters of
3, 5.23, and 7.46 ft. The other set had polygon diameters
of 6, 10.45, and 14.9 ft. These sizes were chosen using
the following rationale. First, the largest polygons in each
set spanned 0.1 and 0.2 visual degrees at the 45-ft altitude.
Second, the smallest polygon in the large set of sizes
spanned approximately twice as much visual angle as the
small polygon in the small set.
Analytical Evaluation of Visual Scenes
Several metrics were used to evaluate the efficacy of using
these procedures in constructing the databases. First, the
number of visible polygons is plotted for each of three 10°
elevation bands (in fig. 63). These three 10° elevation
bands between 0° and 30° below the horizon nearly span
the field of view represented by the cockpit windows. As
shown, in the high condition, the number of polygons
visible is nearly constant versus altitude for each elevation
band. However, it is difficult to compare the medium and
low condition in terms of the level-of-detail represented.
For the medium condition, the number of polygons
visible always increases with altitude, but in the low
condition, the number of polygons in the 0°–10° elevation
band decreases with increasing altitude.
51
Figure 63. Visible polygons for the three level-of-detail visual databases.
Another metric, mean visual form ratio, was also
examined. Mean visual form ratio is the average angular
width divided by angular length. It is simply a function of
altitude and range to an object. Figure 64 shows that the
mean form ratio was invariant for the high condition,
decreased slightly with altitude for the medium condition,
and dramatically decreased with altitude for the low
condition.
Motion System Configurations
Two motion configurations were presented: full motion
and no motion.
Procedure
Five NASA Ames test pilots flew the two tasks. All of
the pilots had extensive rotorcraft and simulation experience. Pilots signaled the initiation and conclusion of the
task by pulling a trigger on the centerstick. The pilots
Figure 64. Mean visual form ratio for the three level-of-
detail visual databases.
participated in one or two sessions per day, with each
session lasting 1 to 2 hr. Pilots received training during
their first session and a short refamiliarization on subsequent sessions. These training sessions used several high
level-of-detail databases constructed especially for training
purposes (they were not used in the data collection). Pilots
evaluated the configurations in a randomized order.
Results: Objective Performance Data
All of the results for the altitude repositioning task were
analyzed using a repeated measures analysis of variance
(ref. 54). For this task, the pilot’s altitude repositioning
error was determined by
% repositioning error =
actual altitude change
desired altitude change
desired altitude change
-
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
(20)
Figure 65 shows the mean percent repositioning error for
three of the five experimental factors: vertical motion
presence, reposition direction, and initial altitude. Across
all visual databases, accuracy in altitude repositioning
improved when motion was present (F(1,4) = 39.347,
p = 0.003), with an overall tendency to overshoot the
desired altitude change. This result was very surprising,
for conventional wisdom would suggest that estimating
the required altitude change would be a purely visual task.
It would be expected, based on the results of Vertical
Experiments I and II, that the trajectory quality between
the two final altitude points would be improved with
motion (motion allowing the generation of lead, thus
improving the open-loop phase margin). However, it was
not expected that the presence of motion would affect the
52
Figure 65. Altitude repositioning error.
altitude endpoint. Apparently the pilot was combining the
visual and motion cues in a way that improved his
estimate of the vehicle’s state.
In comparing climbs versus descents, pilots were better at
halving the altitude than they were at doubling it (F(1,4) =
23.339, p = 0.008); there was a tendency to ascend too
high when doubling altitude and to descend too little when
halving altitude. Finally, figure 65 shows that better
accuracy resulted when starting at higher initial altitudes
(F(1,4) = 14.064, p = 0.002); there was an overall
tendency to overshoot the desired altitude. Interestingly,
there were no statistically significant main effects found
when varying the level-of-detail quality among the
databases, nor for the minimum resolution size.
No main effects were found for visual scene level-of-detail
manipulations, but there was a statistically significant
interaction between level-of-detail and initial altitude
(F(4,16) = 3.451, p = 0.032). Figure 66 illustrates this
interaction. For the ascents, the final altitude more closely
matches the desired doubled altitude as the level-of-detail
becomes more constant. Only for the 21-ft initial-altitude
descents did the high constancy level-of-detail database not
result in the best repositioning performance. So, it
appears useful to attempt to have the visual system mimic
the level-of-detail changes, as one would experience in the
real world.
Altitude-Rate Control Task. The performance
measurement for the altitude-rate control task was mean
absolute vertical rate during the climb or descent. Only
data between vehicle altitudes of 10 and 40 ft were used,
thus eliminating the initiation of either the climb or the
descent.
Figure 66. Altitude reposition versus level-of-detail and
initial altitude.
Figure 67. Vertical speed dependence on motion and
movement direction.
Statistically, there were significant effects for the presence
of vertical platform motion (F(1,4) = 78.846, p = 0.001)
and for movement direction (F(1,4) = 14.806, p = 0.018).
There was also a significant interaction between these two
factors (F(1,4) = 12.379, p = 0.024). Figure 67 shows
these effects. Note that the vertical rates were slower with
motion than without motion, and that the vertical rates
were slower when descending than when climbing. The
presence or absence of platform motion had a stronger
effect on performance than the movement direction.
53
Figure 68 shows the mean vertical speeds versus altitude
for all of the pilots for the four combinations of movement direction and motion presence. These profiles
illustrate that for climbs, vertical speed increased with
increasing altitude, and that the increase was more
pronounced when motion was absent. What seems to be
occurring is that the presence of platform motion reduces
the influence of optical flow rate changes on a pilot’s
control of vertical speed. Optical flow rate when moving
vertically (the angular rate at which objects move visually
in elevation) is proportional to vertical speed divided by
altitude (ref. 66). So, at constant vertical speed, optical
flow rate continuously decreases during a climb. If pilots
try to maintain a nearly constant optical flow rate, they
will increase speed with increasing altitude.
The theory that pilots try to maintain a constant optical
flow rate is supported by Johnson and Awe (ref. 67).
They found that during a fixed-base simulation in which
pilots were asked to maintain speed that the pilots often
slowed as their altitude decreased. Since Figure 68 shows
this same tendency, but less so with motion, it is believed
that the acceleration cue mitigates the attempt to maintain
a constant optical flow rate. Manipulations in level-ofdetail did not affect the pilots’ ability to control vertical
rate.
Summarizing the experiment discussed in this section,
platform motion improved pilots’ accuracy in the altitude
repositioning task, which was surprising. It is hypothesized that an integration of the visual and the motion cues
is occurring, and that the integration affects a pilot’s
estimate of altitude and altitude rate. The specifics of this
integration are still unknown. That is, future work is
needed to determine how many of the altitude and altituderate cues are derived from the visual and how many are
derived from the motion system. Still, this study showed
that one cannot ascribe any of the vertical states to a
single cue. The overall conclusion is that for the control
of altitude in simulation to be more like that of flight,
vertical motion should be provided.
Figure 68. Mean vertical speed versus platform motion and movement direction.
55
7. Roll-Lateral Experiment
Background
In flight simulation, the roll and lateral translational
degrees of freedom are treated together for several reasons.
First, owing to the motion platform geometry, a certain
amount of lateral translational motion must accompany a
rolling motion to locate the center of rotation properly.
Second, for coordinated maneuvers, in which the body-axis
lateral aero-propulsive forces are zero, lateral translational
platform motion provides an acceleration to counteract the
“leans” that would result from only rolling the cockpit.
Figure 69 shows a coordinated and an extremely
uncoordinated case (roll only) with a pendulum hanging
from the top of a rolling flight simulator cockpit. In both
cases, altitude remains constant. In the coordinated case,
the platform moves laterally with an acceleration of
g*tanfplat; however, the body-axis lateral component of
the aero-propulsive force is zero which keeps ay = 0.
Sustaining the platform acceleration to maintain this
coordination consumes available lateral displacement
quickly.
Coordinated
Lift
Cockpit
Yplat = gtanfplat
Uncoordinated
ay = 0
fplat
Yplat = 0
Lift
g g
ay = –gsinfplat
fplat
Figure 69. Coordinated vs. uncoordinated flight.
Most motion-base flight simulators are hexapods with
similar displacement capabilities. In these devices,
sufficient lateral translational platform travel is not
available to simulate exactly the motion cues that a pilot
would receive in coordinated flight. To reduce the amount
of lateral displacement used, platform drive commands use
several methods. In one method, the roll angle of the
motion platform is reduced relative to the model (and thus
the visual scene). This requires less lateral travel, since
less lean-due-to-roll motion is present. In the second
method, less than the full amount of lateral translational
motion required for coordination is used. The purpose of
his study was to investigate the trade-off between these
two options.
Experimental Setup
Task
Constant-altitude lateral side steps were performed between
two points 20 ft apart. A positioning sight in the visual
scene allowed pilots to use parallax to determine their
positioning error precisely. The desired positioning
performance standard was ±3 ft about the desired hover
point (which was 20 ft away), and the adequate performance standard was ±8 ft. The task had to be completed in
less than 10 sec for desired performance and in less than
15 sec for adequate performance. Pilots pressed an event
marker on the center stick when they felt they had acquired
the station-keeping point. They were instructed that pushing this button meant that they were within the position
error for desired performance, and that they believed they
would remain within the desired performance standard.
Simulated Vehicle Math Model
The math model had only two degrees of freedom. Altitude
remained constant at 25 ft; heading, pitch, and
longitudinal position remained constant at zero. Pilots
controlled the vehicle with lateral displacements of a
center stick only. The equations for motion were
.. . . . f f d = - + 4 5 1 7 lat (21)
. sin v g = f (22)
where v is the body-fixed velocity in the y direction
(lateral). These equations represent a typical helicopter
math model that is fully coordinated at the aircraft’s
c.m., since no lateral aero-propulsive forces are present
( a v g
y = - . sin f). Actual helicopters have a drag owing to
lateral translational velocity, and they also produce a side
force at the rotor which contributes to a rolling moment.
Each of these real-world effects causes uncoordinated flight
during a side-step maneuver. This experiment used a fully
coordinated model, since the objective was to examine the
effects of uncoordinated simulation cues caused by simulator platform displacement limitations. Thus, these realworld effects were intentionally absent. So the model
represented a vehicle in which only applied torques created
rolling motion (similar to an AV-8B-like concept) with
no drag owing to velocity (which is small near hover).
For the experiment, the pilot’s abdomen was located at the
aircraft’s c.m. The roll-axis dynamics given above, when
combined with the visual system delay of 60 msec, had
56
satisfactory (Level 1) handling qualities as predicted by
the U.S. Army’s Rotary Wing Handling Qualities
Specification (ref. 50).
Simulator and Cockpit
The experiment again used the NASA Ames Vertical
Motion Simulator, but only the roll and lateral axes. The
lateral axis has ±20 ft of travel, and the roll axis has ±18°
of displacement. The roll and lateral axis dynamic characteristics were dynamically tuned with feedforward filters in
the motion software to synchronize the two axes as much
as possible. For this experiment, each motion axis had an
equivalent time delay of approximately 60 msec, as
measured using techniques developed by Tischler and
Cauffman (ref. 46). The cockpit was configured with a
center stick only. No instruments were present, so the
pilot had to extract all cues from the motion system, the
visual system, and the inceptor (center-stick) dynamics.
The center-stick dynamics were measured to be
dlat
lat
F
s
s s
( )
. ( . )
=
+ +
1
0 6
8
2 0 8 8 8
2
2 2
(23)
where dlat and Flat are the displacement and force,
respectively, at the pilot’s grip.
For the visual system, the Evans and Sutherland ESIG
3000 image generator was used. The visual time delay was
adjusted so that it was 60 msec in order to match the
equivalent motion time delay in the roll and lateral axes.
The simulator cab was the same as that used in section 4,
so the field of view is that shown in figure 36.
Motion System Configurations
Figure 70 shows the relevant motion-platform drive laws.
That is, the simulator-roll-angle command differed from
the math model only by a gain (i.e., no frequencydependent motion attenuation from a washout filter was
present). The platform moved laterally to reduce the false
lateral acceleration caused by platform roll angle. Only a
gain was applied on this lateral translational platform
command as well. In practice, both a gain and a high-pass
filter are used, but these effects were not examined.
Kroll g*Klat
fplat
f Yplat
Figure 70. Motion platform logic for roll/lateral task.
As such, the cues felt by the pilot were all in phase with
the visual scene. Usually, this is not the case, because
high-pass (washout) filters introduce a distortion between
not only the motion and visual cues, but also between
motion cue axes.
For small angles, the motion system mechanization of
figure 70 results in a false lateral specific force given by
a g K K
y lat roll
plat
= - ( ) 1 f (24)
Thus, as expected, decreasing lateral translational gain
increases the false cue. However, for a given Klat, increasing the roll gain, Kroll, also increases the false lateral
translational cue. So, increasing the roll gain improves
the fidelity of the roll acceleration cue, but if the subsequent lateral translational motion is not coordinated, the
improvement comes at the expense of increasing the false
lateral translational cue.
Table 4 shows the combinations of roll and lateral
translational motion gains that were tested. Figure 71
shows these combinations with arrows indicating predicted
fidelity trends for the variations in the motion gains. As
Kroll increases, the fidelity of roll accelerations and rates
would increase, but at a given Klat, coordination would
decrease, as the leans resulting from roll would increase.
From the earlier equation for ayplat , the false lateral
specific force for a given roll attitude decreases along the
diagonal.
Table 4. Motion gains tested for roll/lateral experiment.
Klat Kroll
0.0 0.0
0.4 0.2
0.4 0.4
0.4 0.6
0.6 0.2
0.6 0.4
0.6 0.6
0.8 0.2
0.8 0.4
0.8 0.6
1.0 1.0
57
1.0
Increasing roll fidelity
Increasing
coordination
0.8
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
Kroll
Klat
Figure 71. Configurations and fidelity effects for roll/lateral
experiment.
Procedure
Three test pilots participated in the study. The pilots were
from NASA Ames, the Federal Aviation Administration,
and Lockheed-Martin. All pilots had significant flight and
simulation experience. The NASA and the FAA pilot had
extensive helicopter experience, and the Lockheed Martin
pilot had experience in hovering jet aircraft.
Pilots practiced with a motion configuration selected
randomly at the beginning of each test period. During the
trials, each of the three pilots evaluated the configurations
in a random order. Pilots rated each configuration after
performing the task with that configuration three times.
Pilots subjectively evaluated the configurations in two
categories: motion fidelity and handling qualities. For
motion fidelity, they used the scale developed by Sinacori
(ref. 44), but with the modifications suggested in Vertical
Experiment I. The scale is shown in table 5. To rate the
handling qualities, the Cooper-Harper scale was used
(ref. 53). An overview of the scale is given in
appendix D.
Table 5. Revised motion fidelity scale.
Fidelity rating Definition
High Motion sensations are like
those of flight
Medium Motion sensations are
noticeably different from
flight, but not objectionable
Low Motion sensations are
noticeably different from
flight and are objectionable
Results
Objective Performance Data
Again, compelling performance differences occurred
between full motion and no motion, as shown in
figure 72. The solid lines in the figure represent full
motion (Kroll = Klat = 1), and the dotted lines represent no
motion. When transitioning from full to no motion,
performance degraded, and the magnitude and rate of
control inputs increased. Pilot-vehicle performance degradation typically becomes more marked as the dependency
on the pilot for control and stabilization increases. The
magnitude and rate of control input increases, because the
pilot is now trying to extract vehicle state information
kinesthetically (via sensing position and forces in the
limb-manipulator system), since the platform cues that
supplied lead information directly (from vehicle acceleration) are now missing. These tendencies are consistent
with the other studies in this report.
Figure 73 illustrates how overall positioning performance
varied with the configurations. Pilots assigned numerical
values to designate performance levels: a 1 for desired
performance; a 2 for adequate performance; and a 3 for
inadequate performance. Since pilots flew three runs for
each configuration, their performance for each one of the
three runs was assigned a value. Thus, if adequate performance was achieved on all three runs, the score was 6.
Mean and standard deviations are shown.
58
0 5 10 15 20 25 30 35 40
-5
0
5
Lateral stick, in
Time, sec
Motion
No motion
-25 -20 -15 -10 -5 0 5
-10
-8
-6
-4
-2
0
2
4
6
8
10
Y, ft
y , ft/sec
Figure 72. Full motion vs. no motion for roll/lateral experiment.
The objective positioning performance varied little with
configuration. Most pilots were able to achieve desired
performance for the runs, except for the fixed-base run.
Two of the three pilots did not obtain desired performance
consistently without motion.
Lateral stick rms position averages are shown in
figure 74. The highest average rms values are for the
fixed-base condition. As the amount of motion increased,
the resulting rms stick position generally decreased.
59
Figure 73. Positioning performance for roll/lateral
experiment.
Figure 74. Rms lateral stick position for roll/lateral
experiment (in.).
Figure 75 shows how rms of the lateral stick rate varied
over the configurations. Each value represents the average
of the three rms values (one for each pilot) for a concatenation of three time-histories (one for each run). The
fixed-base condition resulted in higher rms stick rates than
the larger motion conditions (Klat = 1/Kroll = 1; Klat =
0.8/Kroll = 0.6; Klat = 0.8/Kroll = 0.4; Klat = 0.6/Kroll =
0.6). Comparisons with and among the remaining
configurations are less clear.
Figure 75. Rms lateral stick rate for roll/lateral experiment
(in/sec).
Subjective Performance Data
Figure 76 shows how the motion fidelity ratings changed
versus roll gain and lateral translational gain. Numerical
values of 1, 2, and 3 were assigned to fidelity ratings of
Low, Medium, and High for determining the averages. All
pilots rated the fidelity of the fixed-base case as Low. In
general, as the amount of motion increased (increasing
both Klat and Kroll) the rated motion fidelity improved.
Interestingly, as Kroll increased for a fixed Klat, fidelity
improved. This situation increases the false lateral specific
force cue, which would suggest a fidelity degradation.
However, it appears this degradation was more than
compensated for by an increase in roll fidelity.
Figure 76. Motion fidelity ratings for roll/lateral experiment.
60
The configuration that received the best ratings was for
Klat = 0.8/Kroll = 0.6. This configuration was rated better
than the 1:1 configuration. A possible reason for the 1:1
configuration not being rated the best could be the amplification of simulation artifacts at the extremes of the
motion-system envelope, as discussed later.
Figure 77 presents the average Cooper-Harper ratings for
all configurations. The trend of these results follows
closely with those of the motion fidelity results. This
consistent trend might be expected, because reductions in
perceived fidelity may lessen performance, increase workload, or both. Sometimes pilots can try to substitute other
cues, such as the kinesthetic cues from the force-feel
systems, for cues that have been reduced or eliminated.
However, if this substitution does not result in reduced
performance, it can likely increase workload. Taking
Cooper-Harper ratings, as discussed in appendix D, is ideal
for capturing such a trade-off.
3.92
(0.80)
4.83
(0.29)
4.58
(0.14)
4.50
(0.50)
3.83
(0.29)
3.58
(0.52)
4.08
(0.38)
5.17
(1.61)
3.61
(0.67)
5.42
(0.63)
3 - Average
(3) - Std. dev.
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
Kroll
Klat
2.83
(1.04)
Figure 77. Cooper-Harper ratings for roll/lateral
experiment.
Substantial differences resulted in going from the best
configuration (again Klat = 0.8/Kroll = 0.6) to the worst
configuration (fixed base). The best configuration elicited
satisfactory handling qualities (Level 1, which are ratings
less than 3.5), and the worst configuration elicited
adequate handling qualities (Level 2, which are ratings
between 3.5 and 6.5). Again, as the amount of motion
increased, the handling qualities improved.
Proposed Roll-Lateral Specification
The motion-fidelity results map well into a combination
of the revised motion-fidelity criteria of Vertical
Experiment I, which are shown in figure 78. Since this
Low fidelity
0.0 0.2 0.4 0.6 0.8 1.0
0
20
40
Phase error (deg)
60
80
High fidelity
Medium
fidelity
Rotational gain
Low fidelity
0.0 0.2 0.4 0.6 0.8 1.0
0
20
40
Phase error (deg)
60
80
High
fidelity
Medium
fidelity
Translational gain
Figure 78. Revised motion fidelity criteria from Vertical
Experiment I.
experiment evaluated the effects of gain, and not the
effects of high-pass filter break frequency, the abscissas in
figure 78 can be combined as shown in figure 79. The
average motion-fidelity ratings for the tested configurations are also shown on this combined specification.
In general, the combined specification matches well with
the data. The dividing line between High and Medium
fidelity would be at 2.5, and the dividing line between
Medium and Low fidelity would be 1.5. An instance that
does not match well is for the Klat = 0.4/Kroll = 0.2
configuration for Pilot 1. This configuration would be
predicted to be on the borderline of Low and Medium, and
this pilot rated the configuration High and Medium in his
two evaluations. The other two pilots rated it Low, which
matches prediction. Interestingly, Pilot 1 decreased his
rating to Low if additional lateral translational motion was
provided at the same roll gain. Because an increase in
lateral translational motion should not decrease the fidelity, the ratings of Pilot 1 for this one point may be due to
a random effect, like the order of configurations that were
presented to him.
61
Figure 79. Proposed combined specification for roll/lateral
gains.
The second poor match is for the full-motion (Klat =
0.4/Kroll = 0.2) configuration. On average, the pilots rated
the fidelity of this configuration Medium. A possible
reason that Medium, instead of High, ratings were given
is that some of the undesired side-effects of providing
extremely large motion were noticed. An example of a
VMS artifact is the rack-and-pinion noise that is proportional to lateral-track velocity. Pilots commented on the
noise, and since it represents a sensation noticeably
different from flight, a rating of Medium fidelity results.
Since the Klat = 0.8/Kroll = 0.6 configuration effectively
results in 48% of the lateral translational motion used in
the 1:1 case, artifacts such as the above are reduced
significantly. Another possible side-effect is the imperfect
high-frequency coordination between the roll and lateral
axes; for high-frequency tracking, using high gains can
reveal a noticeable, but not objectionable, sensation (i.e.,
the definition of Medium fidelity). Still, with these two
exceptions, the criterion in figure 79 correlates well with
the data.
63
8. Discussion of Overall Results
General Discussion
These experiments showed the powerful effect that
platform motion has on pilot-vehicle performance and on
pilot opinion in flight simulation. Often, the quantitative
measures have supported the pilot’s subjective measures,
which adds confidence to the results. In addition,
frequency-response analyses offer an explanation of how
the characteristics of the motion filter affect closed-loop
performance.
The powerful effect of motion was shown even when the
pilot was creating his own motion, which was the case in
all of the tasks except the disturbance-rejection task
discussed in section 5. This result conflicts with the views
of Hunter et al. (ref. 14) and Puig et al. (ref. 15). The
difference between these two views is almost certainly a
result of differences in vehicle dynamics. At low speed,
fixed-wing aircraft on stabilized paths do not require as
much compensation from a pilot as do helicopters. In
addition, the tasks that helicopters perform often require
the pilot to control all six degrees of freedom of the
vehicle simultaneously. Improved fidelity of external cues,
such as motion cues, aid in the effective pilot control of
the vehicle. Thus, motion requirements cannot be defined
by task alone. Both the task and the vehicle must be
considered in concert.
This report has illustrated the performance and opinion
differences that arise when simulator motion is provided,
but it has not shown if there is any training benefit to the
use of motion. It is possible that training in a more
difficult task environment (no motion) may actually
shorten or improve training. For instance, learning to
balance a short inverted pendulum can train a person to
balance a long inverted pendulum easily. However, if lack
of motion causes a bad habit to be learned, then that
would certainly argue against training without motion.
The effectiveness of training without motion would
require a careful transfer-of-training study.
Among the motion configurations tested, it was the
translational motion that always had a strong effect. Yaw
rotational motion was shown to be a redundant cue, and
less roll motion was more acceptable (when comparing
percent of full motion) than less lateral translational
motion. Requiring less motion in the rotational axes is
still consistent with the revised set of the Sinacori criteria
that were suggested in this report.
A question arises as to why only one of the three
rotational cues is redundant. That is, why is yaw rotational motion redundant, but pitch and roll motion useful?
A possibility is that the pitch and roll rotational cues (as
sensed by the inner ear) are no more important than the
yaw rotational cue, yet their usefulness arises from the
additional cues concomitant with pitch and roll motion.
These additional cues have two sources.
First, pitch and roll motion cues interact with the gravity
vector, as discussed in section 7. Very few simulators can
remove the specific force cue that arises from either pitch
or roll attitude. As such, evaluating the effect of the
angular cue only is challenging; it has only been investigated by Jex et al. (ref. 41) in which subjects rolled while
lying on their backs (thus the gravity vector did not
change relative orientation during an orientation).
Although that study showed an effect of roll, this could be
due to another factor (in addition, it might be argued that
compelling roll visual cues were not present in that study,
as only a horizon line was present).
The other factor is the tangential acceleration that results
from the moment arm between the roll center of rotation
and the motion sensors on a human. It is not possible to
eliminate the effect of these tangential accelerations
completely, for the human motion sensors are in different
locations (inner ear, neck, buttocks, limbs). Some
experiments have isolated the head by fixing it in an
apparatus and subsequently performing reorientations
about that axis, but those were not piloted experiments.
Thus, when pitching and rolling, isolating the angular cue
from the translational cue is difficult if not impossible. It
is only in yaw that many of these cross-coupling effects
into the translational axes are lessened (but perhaps not
removed completely, as discussed in sec. 3). Thus, the
above reasons may explain why the requirements on the
yaw axis are different from pitch and roll.
Although the longitudinal axis was not examined in these
studies, no reason is offered as to why the requirements in
that axis might be different from those in either vertical or
lateral translation.
64
Proposed Fidelity Criteria versus Results of
Previous Research
Since a cornerstone of the results presented herein was the
modification and validation of the fidelity criteria in
figure 78, placing those criteria in the context of other
work is important. The results of previous work are
discussed in section 1, and figure 80 correlates the previous work with the new criteria suggested here. In
figure 80, the points tested and found to have at least
adequate fidelity are shown. The word “adequate” is chosen
in an effort to make a consistent comparison with the
earlier work. Much of the earlier work attempts to define a
boundary between adequate and inadequate rather than
breaking down adequate into three categories such as High,
Medium, and Low fidelity. However, it is appropriate that
the user should strive to stay away from Low fidelity,
which would certainly be termed as inadequate, for the
motion sensations are objectionable. Thus, in comparing
the previous work with the proposed criteria, an
inconsistency would be present if previous work stated
that an “adequate” motion system setup was in the
proposed Low fidelity region.
In the rotational axes, the criteria apply only to pitch and
roll; yaw was found to be redundant. Only the work of
Bergeron (ref. 38) evaluated a range of yaw configurations,
which was discussed earlier. The only inconsistency
between the previous work and the criteria suggested is the
work by Shirachi and Shirley (ref. 42). Their experiment
did not vary motion-filter natural frequency, which
provided 62.5° of phase distortion at 1 rad/sec, which is
2.5° in excess of the Medium fidelity boundary. Still, the
boundary at 60° should remain, for both the work of
Stapleford et al. (ref. 26) and Bray (ref. 27) support it. If
Shirachi and Shirley had evaluated conditions with less
phase distortion, those conditions might have been
preferred.
In the translational axes, a “region of uncertainty” extends
into the Low fidelity region; this does not suggest an
inconsistency, however, because that area simply was not
evaluated by Jex et al. (ref. 43). However, a “delayed side
force” region cuts off an area in Medium fidelity. The
delayed-side-force region applies to the sway axis when
that axis is used to eliminate the specific force that arises
from platform roll. That region was not explored in
section 7, and it may merit additional examination.
However, the delayed-side-force region is certainly adequate
when trying to represent true math model cues, as shown
in the vertical experiments discussed in sections 4 and 5.
There is another inconsistency in the translational axis
when compared to the results of Cooper and Howlett
(ref. 40). The displacement of their simulator was clearly
limited as shown by their boundary “too much displacement.” And their report states “Pilot criticism to motion
anomalies during returns from steady maneuvers still is a
problem.” So, their region of “best compromise” is likely
to be based on their simulator’s capability. It is interesting to note that their rotational axis filter is well within
the High fidelity region. Since their platform is synergistic (angular motion usurps translational motion and
vice versa), figure 80 suggests that they might relax their
angular motion in order to gain translational motion. This
change might allow both angular and translational axes to
be in the Medium fidelity region for some maneuvers,
rather than one in High and one in Low. The remaining
comparisons in figure 80 for the translational axes are
favorable. In general, the suggested criteria are reasonably
consistent with those of previous work.
65
Low fidelity
Shirachi-Shirley (ref. 42)
van Gool (39)
SS
Bray (24)
C
b b b
Cooper-Howlett (40)
SS
V
SS
0.0 0.2 0.4 0.6 0.8 1.0
0
20
40
Pitch/roll
60
80
100
High fidelity
Medium
fidelity
Rotational gain
Low fidelity
Medium
fidelity
High fidelity
Motion cues
inadequate
Cooper-Howlett (40)
Best
compromise
Cooper-Howlett (40)
Too much
displacement
Cooper-Howlett (40)
0.0 0.2 0.4 0.6 0.8 1.0
0
20
40
Phase error (deg) Phase error (deg)
Translational
60
80
100
Rotational gain
"Delayed side force"
Jex, et al. (41)
S
Stapleford, et al.
S
V
V
B
B
B
B
"Acceptable"
Jex, et al.
Region of
uncertainty
Jex, et al. (41)
Bray (27)
Jex, et al.
Too much
displacement
Jex, et al. (41)
"The leans"
Jex, et al. (4.1)
Bergeron (38)
Figure 80. Comparison of previous work with suggested criteria.
66
A General Method for Configuring Motion
Systems
Based on these experiments, on the experience attained in
their development, and on the discussion above, figure 81
suggests a step-by-step method for users who configure
motion systems. In figure 81, boxes that are dashed, and
underlined words in the boxes, represent suggested
additions to current practice as a result of the research
described in this report. This method is in contrast to the
current trial-and-error method that is often used by motiontuning experts and in subjective pilot evaluations. A
recent improvement to the trial-and-error method has been
suggested through the use of an expert system by Grant
and Reid (refs. 68, 69). However, their method does not
explicitly use motion-fidelity criteria as does the method
proposed here.
First, the task needs to be analyzed to determine if
fundamental motion frequencies are present. Some tasks
have a predominant task frequency. For instance, nap-ofthe-Earth flight over regularly varying terrain can
necessitate low-frequency heave cues (the frequency
determined by hill separation divided by vehicle forward
speed). In these instances, the motion-filter natural
frequency and gain should not be set independently in an
attempt to satisfy the motion-fidelity criteria. Otherwise,
pilots do not feel like they are falling or climbing when
the visual scene indicates they are falling or climbing.
Setting the motion-filter natural frequency should take
priority over setting the gain. The filter natural frequency
should be selected such that less than a 30° phase error at
that predominant task frequency exists. Then, the motion
gain can be selected by trying to maximize the criteria
shown in figure 78. Once these two parameters are
selected, the motion-filter configuration should be
evaluated with a pilot flying the task in simulation to
determine if simulator excursions reach their physical
limits. If the physical displacement limits are reached,
Start
Is motion fidelity acceptable?
Yes
Yes
No
No
Done
Evaluate to
determine if you are
within motion limits
Are there
fundamental motion
frequencies?
Adjust
task
Set motion gain and
natural frequency in
each axis per criteria
Set motion gain
per criteria
Task
Set motion system w
to give < 30° phase
error there
Figure 81. Method for configuring motion systems.
67
then the motion gain should be lowered until the ensuing
motion remains off the limits.
If fundamental motion frequencies do not exist, then the
gain and natural frequency of the motion filter can be set
independently. In this instance, the highest fidelity should
be sought in accordance with the criteria in figure 78.
Iteration with a pilot still needs to be performed to
determine the reachable fidelity level. At the end of either
of these paths, a certain achievable fidelity level will be
reached.
If the desired fidelity level cannot be achieved for a given
simulator’s displacement capability, then the user should
consider changing the task. Often the task is considered a
given and is treated as inviolate, since its origins may be
from flight test experience. However, trying to extrapolate
simulator results to flight when the simulator can only
duplicate the task with low fidelity will usually result in a
poor extrapolation.
Poor extrapolations from simulation to flight have
occurred frequently because of a control sensitivity that is
too high. Pilots improperly perceive vehicle sensitivity to
control stick inputs if tasks are not developed in concert
with a simulator’s displacement capability. Rather than
change the task, the simulator’s gain is reduced, which
gives the pilot a false reading of the true sensitivity.
If the process of developing a task is considered as a
feedback process with the attained simulator fidelity as a
performance metric, then modifying both the motion cues
and the task itself should be considered. It may be the only
method for achieving high simulation fidelity.
Extrapolations to flight can then be made with improved
confidence.
Generally, this method should serve as a useful guide for
configuring motion systems and for selecting tasks to
minimize differences between simulation and flight.
Although the method is iterative, it is possible that its
combination with the expert system ideas of Grant and
Reid (refs. 68, 69) might prove useful.
69
9. Conclusions
Summary
The five piloted helicopter simulation experiments
described in this report produced several important results.
Four of the six degrees of freedom were examined: roll,
yaw, lateral, and vertical. These experiments were
performed on the world’s largest displacement flight
simulator, making it possible to perform many of the
tasks with the motion and visual cues matching exactly.
Using these experiments as a baseline allowed the effects
of degraded motion cues to be examined.
Representative helicopter math models were used; in some
cases, the models were derived from flight test data.
Experienced test pilots were the experimental subjects, and
both objective and subjective data were employed in
developing the principal conclusions that follow.
1. Yaw rotational platform motion has no significant
effect on hovering flight simulation. The presence of
yaw platform rotational motion did not contribute to
significant improvements in pilot-vehicle performance, control activity, pilot-rated compensation, or
pilot-rated motion fidelity.
2. Lateral translational platform motion has a significant
effect on hovering flight simulation. In contrast to
yaw rotational cues, the presence of lateral translational motion did contribute to improvements in
pilot-vehicle performance, reductions in control
activity, lower pilot-rated compensation, and higher
pilot-rated motion fidelity.
3. Lateral translational platform motion combined with
visual yaw cues made pilots believe yaw platform
rotational motion was present when it was not. This
visual and motion cueing combination gave pilots a
strong sensation that both rotational and lateral
translational motions were present. It is believed that
more rapid vection was produced by the lateral
translational motion cue. Thus, for hovering flight
simulation, one should represent the lateral translational acceleration cue, for it is important to all the
aspects of pilot-vehicle performance and pilot
opinion. The same point cannot be made for yaw-axis
rotational motion. As such, since many of today’s
hexapod motion platforms achieve both lateral
translational and yaw rotational motion with the same
set of limited-travel actuators, a high weighting
should be placed on representing the former cues
rather than the latter. An alternative to modifying
today’s hexapod motion platforms is to develop a new
simulator configuration that emphasizes limited
translational motion. Because lateral and vertical
translational motions are the most prevalent, placing
a cab on the end of a cantilevered arm that can rotate
in azimuth and elevation would provide these
motions. Different length arms could be used
depending on the vehicle capability, the evaluation
requirements, or both.
4. Vertical platform motion has a significant effect on
pilot-vehicle performance, control activity, and
motion fidelity. Pilots were surprised at the performance results and at how their technique had to
change when all motion was removed. Two of the
three pilots made collective inputs in the wrong
direction when flying fixed base. Until the value of
vertical motion was demonstrated, pilot subjective
impressions were that the vertical task was primarily
visual. Thus, caution should be used when interpreting piloted subjective impressions of the value of
motion. From these vertical-axis results, a revised
specification was developed that better correlated with
existing experimental results.
5. Vertical motion cues affected altitude estimation.
Pilots, when asked to double or halve their altitude,
had their performance significantly affected by vertical
platform motion. With vertical motion, pilots more
accurately doubled and halved their altitude. Until
now, it was generally accepted that steady-state
altitude estimation was derived from visual cues only.
A hypothesis is that the pilot estimates vehicle state
using all the available sensory inputs. As a part of
this, some significant weighting is applied to
acceleration cues that provide a component of the
pilot’s position estimate.
6. A specification for roll-lateral motion requirements
was developed. For a side-step task that exactly
reproduced motion and visual cues, significant
performance and opinion differences resulted as
motion was removed. A combination of translational
and rotational gain specifications provided a good
prediction of motion-fidelity ratings.
Recommendations for Future Work
1. Two additional degrees of freedom, pitch and surge,
should be examined. The results for these two coupled
axes are expected to be similar to those of
the coupled roll and lateral axes.
70
2. The specification demarcations between high,
medium, and low fidelity were determined based on
the granularity of the points tested. Further efforts are
needed to determine the curves that divide the fidelity
regions more precisely.
3. Controlled experiments should be performed on
several representative hexapod platforms to quantify
the performance benefits of allowing increased motion
in the other axes when turning the yaw rotational
displacement off.
4. Continued attempts should be made to model the
pilot-vehicle system in the simulator environment.
Several unusual results, such as the performance
degradation with the addition of yaw rotational
motion and the improved estimation of height with
the addition of vertical motion have been shown here.
Future models need to account for these findings.
5. Does learning to fly a helicopter on a substandard or
suboptimal motion platform increase total training
costs, or does it pose a safety hazard? To answer these
questions, a careful transfer-of-training study needs to
be performed.
71
Appendix A—Human Motion Sensing
Characteristics
Models of how the semicircular canals and the central
nervous system combine in the perception of angular
movement have been treated in several research summaries
(refs. 28–31). Subtle differences exist among the model
structures reviewed. Significant differences, sometimes by
several orders of magnitude, exist among measured
numerical values in the respective structures. This
appendix discusses factors that are important in pilotvehicle dynamics modeling and in understanding human
perception of motion in flight simulation.
A model for angular velocity sensation is shown in
figure A1. The semicircular canals are roughly orthogonal
to each other, and each behaves like an overdamped
torsional pendulum (ref. 32). The output of each block is
the deflection of the canal’s cupula, and when it reaches a
threshold deflection, a sensation develops. Van Egmond
et al. (ref. 32) determined the time constants for yaw to be
0.1 sec and 10 sec. The low-frequency poles shown in
figure A1 are those determined in a later study by Jones
et al., who assumed that the torsional pendulum dynamics
developed a sensation of angular velocity (ref. 33). The
dynamical differences among axes do not have a satisfying
physical explanation and may lie at the behavioral level as
suggested by Zacharias (ref. 31). The delay from the
central nervous system was included by Levison et al.
(ref. 35) for the yaw axis and was carried over to the other
axes. The thresholds shown are based on a summary given
by Zacharias (ref. 31).
Differences among individuals have been noted in both the
dynamics and the thresholds, and it is known that the
thresholds can vary up to an order of magnitude, depending
on whether a subject has to perform a task (such as flying
a simulator) (ref. 36).
While the semicircular canals act as effective rate gyros,
the utricles in the inner ear act as effective linear
accelerometers. Peters (ref. 28) provides a block diagram
of the sensing path for the utricles, which is shown in
figure A2. The transfer function was developed by Meiry
(ref. 22) with a subject experiencing longitudinal motion
only. No dynamic data have been determined in the
vertical or lateral axes. The dynamics of the utricles act as
a bandpass filter between 0.1 and 1.5 rad/sec. The cutoff
frequency of 1.5 rad/sec suggests that high-frequency
accelerations must be sensed by the tactile mechanisms
in the body and not by the vestibular system. A wide
variance exists in the literature for the translational
specific force sensing threshold, which is dmin in
figure A2. Peters reviewed thresholds from seven sources
and found that they ranged from 0.002 and 0.023 g’s
(ref. 28).
In addition to these vestibular models, nonvestibular
motion sensing plays a prominent role in motion
perception. Gum states that “For man in flight the
component of the vestibular apparatus, semicircular canals
and otolith, do not seem to be very reliable or useful
force- and motion-sensing mechanisms” (ref. 30). He
summarizes nonvestibular models, with a model of the
control of lateral head motion shown in figure A3. Here,
the head is essentially an inverted pendulum with respect
to the pilot’s body that is strapped into a moving cockpit.
Taps of the physiological feedback system that regulates
the head position serve as an effective motion cueing
source. The closed-loop dynamics of the head-control
model have a real-axis pole at 3 rad/sec with the rest of the
poles at frequencies higher than 10 rad/sec. Thus, the
bandwidth of the head-positioning control is twice that of
the vestibular system.
The final sensing model covers body pressure sensing;
very few quantitative data are available to describe its
dynamic response. The body-pressure model shown in
figure A4 is from Gum (ref. 30); it has a natural frequency
of 34 rad/sec. This bandwidth would make the body’s
pressure response dynamics the highest of all of its
motion-sensing capabilities. The 1-sec time-constant
high-pass filter in the model is due to the adaptation effect
wherein the receptors in the skin lose their sensitivity to
sustained acceleration.
These models represent the latest research findings in the
field of motion sensing, but they are incomplete and have
limitations. Zacharias points out that there have to be
studies to develop an integrated cueing model (ref. 31).
To summarize, substantial work in human sensory
modeling has provided useful, but incomplete, information for predicting motion platform effects on pilotvehicle performance and workload. Rather than use these
detailed sensory models, appendix B illustrates that some
useful trends can be predicted by using a higher-level
structural model of the pilot. Yet, as will be shown, this
model fails to predict a pilot’s sensitivity to some key
motion parameters. All of this points to the need for
additional empirical data.
72
s
Pitch
1
3.6
(s+0.19)(s+10)
e–0.3s
s
Roll
Body-axis
pitch, roll,
and yaw
rates
Effective
torsional
pendulum
dynamics
Central nervous
system delay
Threshholds,
deg/sec
Subjective
angular
velocity
1
2.5
(s+0.16)(s+10)
e–0.3s
s
Yaw
1
1
1
1
4.2
(s+0.098)(s+10)
e–0.3s
Figure A1. Model of angular velocity sensation.
K
d
F(s)
Subjective
acceleration
and/or
orientation
to apparent
vertical
Central
nervous
system
Thresholds, ft/sec2 Utricles
0.15
Gravito-inertial
force vector in
earth-fixed frame
K
d
(s+0.1)(s+1.5)
Transformation
from earth to
head frame
1
1
d
min
Figure A2. Model of specific force sensation.
73
+
+
+ 1330
Desired
head
roll
angle
(s+12.5)
–
+ 1
Head Muscle
Specific torque
due to gravity
5(s+4)
(s+20)
Muscle spindle feedback
Head
roll
angle
Head
roll
inertia
specific
torque
(disturbance)
s
2
+10s+7.81
2
73.8
Figure A3. Lateral head motion control model.
Figure A4. Body-pressure sensing model.
75
Appendix B—Height Regulation
Analysis with Previous Model
To acquire insight into the potential effects of motion on
a pilot during the performance of a task, a state-of-the-art
analytical model was used. The model was applied to the
experiment described under Vertical Experiment I (sec. 4).
The task in that experiment was an altitude reposition
during hover, so it was a single-axis task. A plausible
interconnection of the relevant system dynamics in the
task is shown in figure B1. Here the pilot desires to attain
the commanded altitude, hc. Based on the motion and
visual cues, the pilot then adjusts his collective control
position dc to zero the difference between his actual and
commanded altitude. All of the elements and connections
in figure B1 are adequately known except for the pilot
element.
h
c
d c
d c
h h h 1
(s)
s
2
Visual
system
Motion
system
Aircraft
Pilot
Figure B1. Altitude reposition block diagram in simulation.
In particular, what is not known is how the pilot uses
the motion and visual cues to estimate vehicle state.
Although the motion system has only acceleration as its
input, and the visual system has only position as its
input, what are each of these system’s effective outputs? It
is reasonable to assume that the motion system provides a
salient acceleration cue and that the visual system provides
a strong position cue. It is often assumed that the visual
system also supplies the velocity cue via the time-rate-ofchange of the displayed positions. And certainly at a
steady-state velocity, the motion system provides no cue.
These very assumptions are made by Hess and Malsbury
(ref. 61) as shown in their “structural model of the pilot,”
which is reproduced in figure B2 in the context of the
altitude repositioning task discussed in section 4.
The pilot is assumed to close loops around vertical
acceleration, vertical velocity, and vertical displacement.
In the model, the acceleration is derived solely from the
motion feedback; however, it might be argued that
acceleration could also be derived from the second timederivative of the displacement. In the acceleration feedback
path, two dynamical elements are shown. The first is a
high-pass motion filter, which attenuates motion at all
frequencies via K and at frequencies below w. Both of
these parameters are adjustable in a given simulation
facility based on the task demands and the facility’s
motion displacement capability. The second dynamic
element is the motion-system servo hardware. Using the
frequency-response testing techniques developed by
Tischler and Cauffman (ref. 46), the simulator has
vertical-axis drive dynamics (approximately) of
..
..
( )
( )( )
( )( )
h
h
s
s s
sim
com
=
+ +
8 26
8 26
(B1)
The vehicle vertical velocity and displacement are derived
from the visual system, which is represented by an
83-msec time delay. Hess and Malsbury point out in
reference 61 that the vertical velocity sensing assumption
affects the primary control loop, but that it is currently
not known how to select the appropriate division of the
vertical velocity estimation between the motion and visual
cues, which is a motivation for Vertical Experiment III
described in section 6.
The pilot gains shown in figure B2 are selected based on a
set of adjustment rules proposed by Hess and Malsbury.
For these tasks, only the high-pass filter was changed, and
using the adjustment rules results in the change of three
variables K
h
.. , K
h
. , and Kh. The gain K
h
.. is determined so
that the lowest damping ratio of any oscillatory roots in
the ..( ) /.. ( ) h s h s
c transfer function is at least 0.15. This
value of damping ratio is selected to represent a trade-off
between stability and high-frequency phase-lag reduction
(ref. 70). In reference 70, Hess points out that requiring an
identical damping ratio of this loop for all configurations
reduces the sensitivity of the modeling procedure to the
particular value chosen.
Then, with the motion loop closed, K
h
. is determined so
as not to violate the combination of a phase margin of 45°
and a gain margin of 4 dB in the vertical velocity openloop . ( ) /. ( ) h s h s
cue e . These values are selected by Hess to
achieve adequate stability margins for the vertical-velocity
loop. Again, Hess states that requiring all configurations
to have the same level of relative stability (same phase
and gain margin) reduces the sensitivity of the model to
the particular values chosen.
76
Pilot
0.370
+
– –
0.434
0.218
+
–
+ +
h
c
h
e
d c
K
h h
c
h h
h
c h
h
cue
h
sim
K
h
K
h
(8)(26)
(s+8)(s+26)
h
filt
Ks
2
Motion
system
Central nervous and
neuromuscular system Aircraft
Visual system
hardware
High-pass
motion filter
Motion servo
dynamics
s
2
+2zws+w
2
187(s+0.2)e
–0.15s
(s+0.05)(s
2
+2(0.35)(20)s+20
2
)
9s
s+0.3
1
s
1
s
e
–0.083s
e
–0.083s
Figure B2. Structural pilot model for Vertical Experiment I (ref. 61).
Finally, the gain Kh is chosen to have adequate frequency
separation between the altitude and vertical-velocity loops
while maintaining good altitude-loop crossover characteristics. A useful rule-of-thumb is to separate the altitude
and vertical-velocity crossover frequencies by a factor of 4
(ref. 71).
This pilot gain determination process was repeated for the
high-pass motion filter variations in Vertical
Experiment I. Although the motion filter gain, K, was
changed in that experiment, note that the adjustment rules
of the model do not account for an effect due to that gain
K. That is because any changes in K are offset by adjustments in K
h
.. . So, the model only predicts performance
differences owing to changes in the motion-filter natural
frequency and not its gain.
For the five motion configurations that encompassed the
motion-filter natural frequency changes, the predictions
of the model are shown in table B1. The motion-filter
configurations, V1, V2, V3, V4, and V10 are fully
described in Vertical Experiment I; however, V1 essentially represents a motion-filter transfer function of unity,
and V2 through V4 represent increasing break frequencies
of 0.245 to 0.885 rad/sec. The V10 configuration has no
motion at all.
The last two columns of table B1 are the altitude-rate
pilot-vehicle crossover frequency and the closed-loop
altitude-rate bandwidth, respectively. The definition of
bandwidth used in reference 61 was that of the –90° phase
point. The frequency responses along with the bandwidth
measure are shown in figure B3.
Note that the analytical model predicts a reduction in the
vertical-velocity closed-loop bandwidth, from 6.36 to
4.27 rad/sec, as the motion feedback degrades from near
full-motion to no motion. For the no-motion case, V10,
the pilot has to increase his visual velocity feedback,
which results in a higher crossover frequency than before,
but in a lower closed-loop bandwidth.
77
Table B1. Analytical pilot-vehicle characteristics.
Configuration Kh
(1/sec)
K
h
.
(1/sec)
K
h
..
. / .
h h
cue e c w
(rad/sec)
. / .
h h BW
c
(rad/sec)
V1 0.323 0.542 0.217 1.20 6.36
V2 0.299 0.565 0.240 1.24 6.24
V3 0.335 0.595 0.215 1.33 6.13
V4 0.385 0.636 0.213 1.50 5.85
V10 0.795 0.725 0.000 3.10 4.27
10
-1
10
0
10
1
-20
-10
0
10
20
Magnitude, dB
V1
V2
V3
V4
V10
10
-1
10
0
10
1
-200
-150
-100
-50
0
Phase, deg
Frequency, rad/sec
Figure B3. Closed-loop vertical-velocity frequency responses.
If the changes in the closed-loop system roots are
examined for the above configurations, the effective heave
damping goes from –1.4 sec
–1
for the full-motion case
(V1) to –1.1 sec
–1
for the V4 configuration. The location
of this root accounts for much of the bandwidth difference.
This root is also the principal root of interest in the
pilot’s control of altitude. The corresponding effects on
the other roots are minimal. The heave damping
argument, however, does not hold for the no-motion case
(V10). In fact, although the bandwidth is less in this case,
the damping of the primary closed-loop roots is good.
The above model also predicts a degradation in closed-loop
performance from degradations in the motion filter alone.
78
Figure B4 illustrates how the closed-loop vertical-velocity
roots migrate as the motion filter changes. However, these
closed-loop poles are those that result when the model’s
gains are fixed at the full-motion (V1) condition for all the
remaining conditions (V2, V3, V4, V10). This situation
was examined to show the effect of the motion filter alone
without pilot adaptation. Only the region near the origin
is depicted, since the poles and zeros far from the origin
exhibit negligible change.
Two effects of the motion filter alone are noticed. First,
the effective heave damping is reduced as the motion
filter’s natural frequency increases. This result also occurs
when the adjustment rules of the model are followed.
Second, pole-zero dipoles form in which the separation
between the pole and zero become more prominent as the
motion filter is made more restrictive (going from V1 to
V4). As the filter natural frequency increases, these dipoles
encroach upon the pilot-vehicle crossover frequency. Thus,
a broad range of integrator-like characteristics in the
crossover region does not occur. So if the pilot wanted to
achieve a similar, but slower, closed-loop response, more
than just a simple gain change on his part would be
necessary. He would also have to adjust his dynamic
compensation, which would likely entail an increase in
workload or a reduction in his opinion of simulator
fidelity. As expected, the closed-loop bandwidth without
using the pilot adjustment rules becomes worse. The
bandwidth of the V10 configuration is 3.60 rad/sec
without the adjustment rules versus 4.27 rad/sec
with them.
Since this model was applied to Vertical Experiment I
(sec. 4), it is interesting to compare the phase-plane timehistories from that experiment (figs. 40–43, 49) with the
phase-plane time-histories that the model predicts. The
model’s predictions for the five configurations analyzed
(V1, V2, V3, V4, and V10) are shown in figure B5.
Although the model shows degradations as the quality of
the platform motion becomes worse, the model poorly
represents the experimental results in two ways. First, the
model introduces a pronounced underdamped oscillatory
-2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Real axis
Imag axis
Decreasing heave damping
V1 V2 V3 V4
V1
V2
V3
V4
Figure B4. Closed-loop vertical-velocity spectrum versus motion filter changes.
79
mode for the V1-V4 configurations, which has a frequency
of 8 rad/sec. This mode is not present in the experimental
results, which show a reasonably smooth response during
the ascent. Second, the model does not predict the oscillatory behavior that occurs experimentally at the terminal
altitude point (85 ft), which is especially prevalent in the
motionless (V10) configuration. The adjustment rules
need to be modified in the model in an attempt to match
the experimental results. This modification is left for
future work.
It should be clear that some key assumptions are made in
the development of this model, such as which parts of the
simulator system provide which cues, and the adjustment
rules are somewhat of an art. The bottom line is that
today’s models are incomplete and certainly not validated.
The lack of adequate analytical models has been noted by
others. Breuhaus pointed out that it is a dubious assumption that one knows all of the important cues and their
interrelationships (ref. 72). Heffley et al. stated that when
a modeler begins to assemble all of the components that
are believed to influence the pilot-vehicle loop in simulation that one notices the fragmentary nature of, and the
serious gaps in, the quantification of the component
characteristics (ref. 73). More empirical results from
systematic and realistic investigations are needed before
useful analytical models can be created.
V1
V2
V3
V4
V10
-10 -8 -6 -4 -2 0 2 4 6 8 10
-2
0
2
4
6
8
10
12
Vertical velocity, ft/sec
Altitude, ft
Figure B5. Predicted phase-plane responses.
81
Appendix C—Example of Repeated-
Measures Analysis
A researcher wants to know if the differences among
collected data are due to a manipulated experimental factor,
or if they are due to random effects. If data are taken from
a random sample of individuals, each of whom has
experienced a different combination of the experimental
factors, then experimental error may be attributed to two
effects. The first effect is sampling error and the second
effect is error resulting from differences among the
individuals.
Repeated-measures allows the differences among
individuals to be accounted for in the analysis. This is
accomplished by having each individual experience each
experimental manipulation. It is a technique often used
when subjects are available for a long period of time, such
as in a research institution. Details of repeated-measures
theory may be found in Myers (ref. 54). Only a brief
overview will be given here. An example of the data
processing involved is given next.
The example uses the pilot compensation ratings for
Task 1 in section 3 (15° yaw rotational capture). A plot
of the data is given in figure 15. Table C1 contains the
average compensation rating given by each pilot for all of
the combinations of translational and rotational motion.
The first column gives the pilot number, 1–6. The second
column indicates if translational motion was present. A
1 means translational motion was present, and a zero
means it was not. Column three indicates if rotational
motion was present. Column four gives the average
compensation rating, where the average is taken over the
repeated runs performed by each pilot. Here the values
correspond to 0 = minimal, 1 = moderate, 2 = considerable, and 3 = maximum tolerable.
The purpose of the statistical analysis is to determine if
pilots provide better compensation ratings for particular
motion configurations. Specifically, it can be determined
if the ratings are influenced by the presence of translational motion, by the rotational motion, or by a combination of the two motions. Although figure 15 suggests
that the translational motion is likely to be the dominant
factor, the statistical analysis provides quantitative
information on how often such variations are due to
random effects. So, the analysis indicates how solid the
inferences drawn from the data are.
Table C1. Average pilot compensation ratings for yaw
experiment Task 1.
Pilot (i) Translational
on (j)
Rotational
on (k)
Average
rating (Y)
1 0 0 2.67
1 0 1 2.25
1 1 0 1.75
1 1 1 1.75
2 0 0 2.25
2 0 1 1.75
2 1 0 0.75
2 1 1 0.00
3 0 0 1.25
3 0 1 1.25
3 1 0 1.00
3 1 1 1.00
4 0 0 3.00
4 0 1 3.00
4 1 0 3.00
4 1 1 3.00
5 0 0 2.50
5 0 1 2.50
5 1 0 0.25
5 1 1 0.00
6 0 0 1.75
6 0 1 2.00
6 1 0 1.25
6 1 1 1.00
To determine whether the differences noted owing to
translational motion are statistically significant, the ratio
MStrans / MStrans/pilot is formed. The numerator of this ratio,
MStrans is the between-groups mean square. The denominator of this ratio, MStrans/pilot, is the population error
variance. The higher this ratio, the more likely that the
differences between the two translational configurations
are due to the effect of translational motion and that they
are not random results. The relations for these terms are:
F
MS
MS
trans
trans pilot
=
/
(C1)
MS
SS
a
trans
trans
=
- ( ) 1
(C2)
82
MS
SS
a n
trans pilot
trans pilot
/
/
( )( )
=
- - 1 1
(C3)
SS
b
C SS SS
trans pilot
Y
trans pilot
j i k
ijk
/ =
&aelig;
è
&ccedil;
&ouml;
&oslash;
÷
- - -
&aring; &aring; &aring;
2
(C4)
where
SS
nb
C
trans
Y
j i k
ijk
=
&aelig;
è
&ccedil;
&ouml;
&oslash;
÷
-
&aring; &aring; &aring;
2
(C5)
and for Task 1 in the Yaw experiment:
SS
ab
C
pilot
Y
i j k
ijk
=
&aelig;
è
&ccedil;
&ouml;
&oslash;
÷
-
&aring; &aring; &aring;
2
(C6)
C
abn
i j k
ijk
Y
=
&aelig;
è
&ccedil;
&ouml;
&oslash;
÷ &aring; &aring; &aring;
2
(C7)
i
j
= =
= =
1 6
0 1
,...,
,
pilot
translational configuration (off or on)
k = 0,1 = rotational configuration (off or on)
a = 2 = number of lateral configurations
b = 2 = number of rotational configurations
n = 6 = number of pilots
Using the values from table C1, then the equations
become
C = =
( . )
( )( )( )
.
40 92
2 2 6
69 77
2
(C8)
SSpilot =
+ +
+ + +
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
- =
( . ) ( . ) ( . )
( . ) ( . ) ( . )
( )( )
. .
8 42 4 75 4 50
12 00 5 25 6 00
2 2
69 77 10 55
2 2 2
2 2 2
(C9)
SStrans =
+
- =
( . ) ( . )
( )( )
. .
26 17 14 75
6 2
69 77 5 43
2 2
(C10)
SStrans pilot /
( . ) ( . ) ( . )
( . ) ( . ) ( . )
( . ) ( . ) ( . )
( . ) ( . ) ( . )
. .
=
+ +
+ + +
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
+
+ +
+ + +
é
&euml;
ê
ê
ù
&ucirc;
ú
ú
- - -
4 92 4 00 2 50
6 00 5 00 3 75
2
3 50 0 75 2 00
6 00 0 25 2 25
2
69 77 5 43 10
2 2 2
2 2 2
2 2 2
2 2 2
55 3 98 =
(C11)
MStrans pilot /
.
( )( )
. = =
3 98
1 5
0 796 (C12)
MStrans = =
5 43
1
5 43
.
( )
. (C13)
F = =
5 43
0 796
6 82
.
.
. (C14)
Statistically, the F-ratio is the distribution of the ratio of
two independently distributed chi-squares each divided by
their degrees of freedom. The degrees of freedom are the
number of independent observations that were summed in
obtaining the chi-square distribution. For the above
F-ratio example, the numerator and denominator have
(a – 1 = 1) and (n – 1 = 5) degrees of freedom, respectively. The resulting F-ratio, for these degrees-of-freedom,
is then be compared against tabulated critical regions to
determine if it is large enough to be considered significant.
If the F-ratio is large enough, then the effect of the
experimental factor is said to be significant. Otherwise,
the differences may be due to random error. In the
example, an F = 6.82 means there are 4.7 chances in 100
that the differences would occur randomly. These low odds
suggest that turning translational motion on and off is
affecting the results.
83
Appendix D—Review of Cooper-
Harper Handling Qualities Rating
Scale
The term “handling qualities” is defined as “those qualities
or characteristics of an aircraft that govern the ease and
precision with which a pilot is able to perform the tasks
required in support of an aircraft role” (ref. 53). The
subjective scale that has become the worldwide standard
for measuring handling qualities is the Cooper-Harper
Handling Qualities Rating Scale (ref. 53), which is shown
in figure D1.
After completing a flying task, the pilot assigns a
numerical rating by proceeding through the decision tree
on the left-hand side of the scale. The decision tree
separates the handling qualities into four categories:
(1) satisfactory (rating<3.5), (2) unsatisfactory but
tolerable (3.5 < rating < 6.5), (3) unacceptable (6.5 <
rating < 9.5), and (4) uncontrollable (9.5 < rating). The
first three categories are referred to as Level 1, Level 2,
and Level 3 handling qualities, respectively.
The first question, “Is it controllable?,” must be answered
in the context of the task. If the pilot can control the
aircraft in order to perform the task, even if it requires his
undivided attention, then the aircraft is controllable.
Otherwise, the aircraft is uncontrollable, and the assigned
rating is a 10.
The second question, “Is adequate performance attainable
with a tolerable pilot workload?,” requires the experi-
menter to define performance standards. Engineers and
pilots, at the beginning of an experiment, jointly decide
on two performance standards: desired and adequate. An
example would be for a pilot to fly a landing approach
while maintaining airspeed to within ±5 knots for desired
performance and ±10 knots for adequate performance.
Returning to the second question, the pilot now
determines not only whether the adequate performance
standard was met, but if the workload was also tolerable.
If the answer to this question is yes, then the task can be
performed with reasonable precision, even though it might
take considerable mental and physical compensation on
the part of the pilot.
If the pilot proceeds to the third question, “Is it
satisfactory without improvement?” he now has to decide
whether the vehicle is good enough as it is for its intended
use or if he thinks it should be changed. The vehicle does
not have to be perfect, just good enough.
When the pilot proceeds to the right-hand side of the scale,
a numerical rating is assigned based on the descriptors of
the aircraft characteristics and the demands on the pilot.
This process is often a balance between the performance
achieved and the compensation required by the pilot in
order to achieve that performance.
The Cooper-Harper Handling Qualities Scale has been
used successfully since 1969, and it is an excellent way to
obtain high-quality subjective data from pilots. These data
often correlate well with the objective data collected.
84
Start
Improvement
mandatory
Major deficiencies
Control will be lost during some
portion of required operation
Is it
satisfactory without
improvement?
Is adequate
performance
attainable with a tolerable
pilot workload?
Is
it controllable?
Deficiencies
warrant
improvement
Deficiencies
require
improvement
Excellent
Highly desirable
Good
Negligible deficiencies
Fair – Some mildly
unpleasant deficiencies
Minor but annoying
deficiencies
Moderately objectionable
deficiencies
Very objectionable but
tolerable deficiencies
Major deficiencies
Major deficiencies
Major deficiencies
Pilot compensation not a factor for
desired performance
Pilot compensation not a factor for
desired performance
Minimal pilot compensation required for
desired performance
Desired performance requires moderate
pilot compensation
Adequate performance requires
considerable pilot compensation
Adequate performance requires extensive
pilot compensation
Adequate performance not attainable with
maximum tolerable pilot compensation.
Controllability not in question.
Considerable pilot compensation is required
for control
Intense pilot compensation is required to
retain control
1
2
3
4
5
6
7
8
9
10
Adequacy for Selected Task or
Required Operation*
Aircraft
Characteristics
Demands on the Pilot in Selected
Task or Required Operation*
HQR
Yes
Yes
Yes
No
No
No
*Definition of required operation involves designation of flight
phase and subphases with accompanying conditions.
Figure D1. Cooper-Harper Handling Qualities Rating Scale.
85
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Ames Research Center
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84
A05
Helicopter Flight Simulation Motion Platform Requirements
Jeffery Allyn Schroeder
To determine motion fidelity requirements, a series of piloted simulations was performed. Several
key results were found. First, lateral and vertical translational platform cues had significant effects on fidelity.
Their presence improved performance and reduced pilot workload. Second, yaw and roll rotational platform
cues were not as important as the translational platform cues. In particular, the yaw rotational motion platform cue did not appear at all useful in improving performance or reducing workload. Third, when the lateral
translational platform cue was combined with visual yaw rotational cues, pilots believed the platform was
rotating when it was not. Thus, simulator systems can be made more efficient by proper combination of
platform and visual cues. Fourth, motion fidelity specifications were revised that now provide simulator users
with a better prediction of motion fidelity based upon the frequency responses of their motion control laws.
Fifth, vertical platform motion affected pilot estimates of steady-state altitude during altitude repositionings.
Finally, the combined results led to a general method for configuring helicopter motion systems and for
developing simulator tasks that more likely represent actual flight. The overall results can serve as a guide to
future simulator designers and to today’s operators.
Flight simulation, Helicopters, Motion platforms
Technical Publication
Point of Contact: Jeffery Allyn Schroeder, Ames Research Center, MS 262-2, Moffett Field, CA 94035-1000
(650) 604-4037

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