300 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. 22, NO. 2, MARCWAPRIL 1992

The Detection of Low-Amplitude Yawing Motion Transients in a Flight Simulator

Al-Amyn Samji and Lloyd D. Reid

Abstract-A perennial problem facing flight simulator design- ers is how to handle motion system transients generated by washout algorithms intended to restrict the travel of the motion- base hardware. Motion cues in the flight simulator provide opportunities for lead compensation on the part of the pilot and thus one must ensure that onset transients are detected by the pilot. Likewise one must ensure that other unwanted motion transients generated by the system are not detected. The present study employs typical washout motion transients in an experiment designed to establish the motion levels required to achieve the aforementioned design goals. A set of critical amplitudes for both onset and return motion are determined in a flight simulator environment. It is found that a significant increase in detection levels occurs when the pilot switches from being a pure observer to actively controlling the simulator.

I. INTRODUCTION HE GENERATION of motion cues in a ground-based T flight simulator is a compromise between the production

of large motions that approximate those of the actual aircraft and the restriction of simulator motion to remain within the physical limits of its motion system. In order to establish the design point in this process it is necessary to determine the simulator motion levels that human pilots judge to be acceptable representations of aircraft motions. In addition, the simulator designer would like to be able to predict what the pilot will sense under specific conditions in the flight simulator.

In general you need to know how large motion must be in order to have the pilot sense the simulated aircraft motion and also how small motion must be in order to prevent the pilot from sensing unwanted simulator motions. Past work on the sensing of low level motion has employed signals with waveforms not representative of those normally found in operational flight simulators. Typically sinusoids and velocity ramps have been utilized. In order to broaden this data base, the present study employed motion waveforms representing the actions of simulator washout filters on the simulated aircraft’s response to sudden control inputs. In a modern simulator, washout filters (implemented in software) are used to interface the simulated aircraft’s motion equations with the simulator’s motion-base hardware. Their main function is to remove low frequency commands to the motion-base that would tend to drive the hardware against its travel limits. The washout filters are formulated as high-pass filters in order to achieve this result.

Manuscript received October 6, 1990; revised July 16, 1991. The authors are with the Institute for Aerospace Studies, University of

IEEE Log Number 9104545. Toronto, 4925 Dufferin Street, Downsview, ON, M3H 5T6 Canada.

TABLE I ROLL VELOCITY DETECTION LIMITS

Source Motion Level (Reference) (de&)

[I1 0.44 PI 0.12 [31 0.30-0.37

TABLE I1 PITCH VELOCITY DETECTION LIMITS

Source Motion Level (Reference) (deds)

[I1 0.29-0.34 131 0.33-0.53 141 1.6G3.00

A careful study into the detection of sinusoidal motion by human subjects was performed by Greig in [ l ] where it was reported that just detectable motion levels have been studied by a large number of researchers over the years. The individual findings depend to some extent upon the details of how the experiments were performed. This accounts for much of the spread in the reported results. The data presented below are related to a reference frame Fp located at the centre of the human subject’s head. The subject is assumed to be oriented vertically with his spine aligned with the gravity vector and the z-axis of the reference frame (positive downward). The x- axis is pointing forward and the y-axis to the right. Roll rate is angular velocity about the x-axis, pitch rate is angular velocity about the y-axis, and yaw rate ( r ) is angular velocity about the z-axis.

The data summarized in Tables 1-111 indicate angular motion levels that the authors felt were just detectable (based on a range of definitions). Fewer results are available for linear motion detection. Reference [2] reports findings from several sources over the range 0.02 to 0.20 m/s2 for all three axes. Reference [3] found detection thresholds for motion along the z-axis ranging from 0.03 to 0.09 m/s2. The previous results were achieved by subjects acting as motion detectors and not actively engaged in the control of any system (such as an aircraft). It was reported in [3] that detection levels could be increased by over 100% when a control task was present during a test.

The yaw degree-of-freedom was selected for the present study because this eliminated the need to consider the effects of tilting the pilot with respect to the gravity vector. Thus it was

0018-9472/92$03.00 0 1992 IEEE

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SAMJI AND REID: DETECTION OF LOW AMPLITUDE YAWING MOTION TRANSIENTS IN A FLIGHT SIMULATOR

TABLE 111 YAW VELOCITY DETECTION LIMITS

Source Motion Level (Reference) (de&)

[21 1.69.0 t51 2.6 [61 2.2-3.1 [71 3.2 181 2 . 5 4 . 0

not necessary to take into account the simulator’s steady-state angular displacement before and after each test. This greatly simplified the experimental procedure.

11. SIMULATOR The experiments were carried out in the UTIAS Flight

Research Simulator shown in Fig. 1. The motion-base is a CAE Series 300 six degrees-of-freedom synergistic unit incorporat- ing hydrostatic bearings. The motion drive algorithm employed was the AW2 adaptive algorithm described in [9]-[ll]. A DC-8 cab is mounted on the motion-base and the whole system is run at a 22-Hz update rate by a Perkin Elmer 3250 digital computer. The out-the-window visual display is viewed through three collimating optical units employing a beam splitter and mirror (from the VITAL I1 system). The field-of- view is 145 degrees horizontally by 30 degrees vertically. The full color display is produced by three Silicon Graphics IRIS 3130 workstations. In the present study the display represented a lead aircraft flying at an altitude of 3000 ft (914 m). This display is shown in Fig. 2. The simulator flight equations represented a Boeing 747 and its implementation is described in [ l l ] . The aircraft weight was set to 500 000 Ibs (227 273 kg) and it was flown in a clean configuration.

The flying task consisted of following the lead aircraft at an indicated airspeed of 300 kts. The lead aircraft was programmed to fly a constant 200 m in front of the piloted aircraft. The lead aircraft performed periodic maneuvers of lateral displacement Y governed by

Y = 7.5 sin wt cos 4 (m) (1)

4 = 0.17coswt (r) (2)

w = 0.0698 (rh) (3)

where q!~ is the lead aircraft roll angle. When tracking the lead aircraft, the pilot carried out mainly rolling maneuvers and hence there was good separation between pilot induced motions and those caused by the yaw motion test signals.

Background motion consisting of broadband random noise was present at all times in the piloted simulator. Uncorrelated noise having the power spectrum shown in Fig. 3 was applied directly to each of the six degrees-of-freedom. The flat portion of the spectrum was 0.0002 (m/s2)2/Hz for translations and 0.66 (deg/s2)2/Hz for rotations. This corresponded to RMS motion levels of 0.03 m/s2 in each translational degree-of- freedom and 1.7 deg/s2 in each rotational degree-of-freedom.

Fig. 1. UTIAS flight research simulator.

Fig. 2. Lead aircraft.

This low-level random motion was included to provide a consistent background motion for all of the test cases. To the pilots it felt like very light turbulence.

111. MOTION TEST SIGNALS

The yawing motion test signals employed in this study were applied about the simulator cab’s z-axis to conform with normal flight simulator practice. This duplicates the approach taken in [3]. The simulator frame FS is parallel to the pilot’s frame Fp and the coordinates of the origin of F p are expressed in Fs components by

Rs = (-0.02 - 0.47 - 1.78)T m. (4)

302 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. 22, NO. 2, MARCWAPRIL 1992

i

0

I I

I I

- rmax I I I I I I

jt4

/ . .-__- - - --

rmin I - tend

t3 010 0.20 0.50 1 2 5 10

f (Hz)

I +z

(a) Fig. 3. Background motion spectrum.

Because the pilot’s head is offset from the yaw axis, yawing motions can potentially stimulate both the angular motion sensors (the semicircular canals) and the linear motion sensors (the otoliths) within the vestibular system. The linear acceleration at the pilot’s head is given by the following equations in the present experiment (based on [12]). In Fs components this linear acceleration as is

r

I- -------------

U: = -xr2 - y+

U; = 0 (7)

where displacements x and y are given by the first two entries in (4). The impact of these accelerations on the detection of simulator yawing motion about the cab’s z-axis will be discussed.

Following the technique developed in [ 131, the test signal angular commands to the motion system were approximations to those generated by washout filters responding to sudden pilot control inputs. The mathematical form of these test signals is described in detail in [14]. Fig. 4 shows a time history of a typical test signal. This was applied to the motion system downstream of the washout filters and did not influence the flight equations. As demonstrated in [15] the UTIAS Flight Research Simulator will respond almost perfectly to such a waveform. Thus no compensation for the dynamic characteristics of the motion system was necessary. The yaw acceleration signal i was made up from four segments.

Segment I : For tl 5 t < t 2 it has the form a l (1 - cosp) with ,8 = 0 at t = tl and p = T at t = t 2 .

Segment2: For t 2 5 t < t 3 it has the form { 2 u ~ + u ~ ( c o s / ? - 1)) with p = 0 at t = t 2 and /? = R at t = t 3 .

Segment 3: For t 3 5 t < t 4 it is linear and at t = t4 the acceleration has returned to zero.

Segment 4: For t 4 5 t < tend it is a transcendental function that smoothly joins to Segment 3 and returns to zero when t = tend after going slightly positive.

The corresponding yaw velocity trace of Fig. 4 begins with an onset motion peaking at a value rmax. This is normally the motion that you wish the pilot to sense as a result of his abrupt control input to the simulated aircraft. This is followed by a return motion peaking at a value rmin. This is generated by the

Fig. 4. Return motion test signal.

washout process returning the simulator to its resting position after the onset of the maneuver, as part of its motion limiting function. Normally you do not want the pilot to sense the return motion. The following signal parameters were selected based on typical washout filter responses found using algorithm AW from [9]:

( t 2 - t l ) = 0.2 s ( t 3 - t 2 ) = 0.2 s

(tend - t l ) = 10 s U1/@ = 0.92.

The remaining parameters were selected to give the desired r,,, and rmin for a particular test with angular acceleration and velocity going to zero at tend.

1V. TASK DETAILS Based on the findings of [3] it was decided to employ a

range of tasks to be carried out in parallel with the motion detection process. In all cases the pilots were secured to the left-hand pilot’s seat by a conventional five-point harness. Three tasks were employed, designed to represent different levels of pilot involvement in the operation of the flight simulator.

Tusk A: In this task the pilot was a passive observer. The window displays were shut off but the instrument panel lights were left on. All other lighting was off. The pilot was instructed to sit erect and to report the interval in a forced choice trial that contained the motion test signal. The pilot did not fly the simulator.

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SAMJI AND REID: DETECTION OF LOW AMPLITUDE YAWING MOTION TRANSIENTS IN A FLIGHT SIMULATOR

Task B: In this task the window displays were on and the pilot was instructed to fly the simulator and track the lead aircraft. The simulator motion system was configured not to respond to the pilot’s control inputs. As in Task A, only the motion test signals and the random background motion were present. The pilot was instructed to sit erect and to report the interval in a forced choice trial that contained the motion test signal.

Tusk C: This task was similar to Task B but with the sim- ulator’s motion system also responding to the pilot’s control inputs.

Each experiment run consisted of 30 forced choice trials. Each trial was divided into two 20-s intervals. During one of the two intervals (selected randomly) the motion test signal was superimposed on the simulator’s current motion. In the case of the onset motion experiment the other interval contained only the simulator’s current motion. In the case of the return motion experiment the other interval contained the onset portion of the test signal superimposed on the simulator’s current motion. At the end of each trial the subject was required to indicate the interval in which he felt the test signal occurred, by pressing the appropriate button on a lap-held response box. During the response phase the window displays were turned off. Audio tones cued the pilot at the start of each interval. An audio feedback tone indicated whether or not his response was correct.

V. TEST SIGNAL AMPLITUDE ADJUSTMENT

The purpose of the forced choice process was to identify critical amplitudes for onset and return motions in the simula- tor. The critical amplitude is defined in the present study to be that motion signal peak angular velocity amplitude that will be detected 76% of the time by pilots performing a forced choice test in a simulator. An adaptive algorithm developed in [l] was employed to determine the critical amplitude. For each trial in the forced choice process the amplitude of the test signal was selected based upon the subject’s performance on all previous trials. This was implemented as follows.

The algorithm for selecting test signal amplitudes contains a psychometric curve representing the probability (@) of a sub- ject detecting a motion test signal as a function of normalized signal amplitude (zlA). Since the exact form of this curve is not available for a given experiment (otherwise there would be no need to perform the experiment) it must be approximated from previous related experimental studies. (In the present case it was based on the results for sinusoidal signals reported in [l]; see Fig. 5.) Fortunately it is found that the results produced are not overly sensitive to the assumed form of (Is). A family of psychometric curves, p ; ( z ) is then produced as

where A; is a free parameter. After each forced choice trial the algorithm employs a least squares technique to determine A; for the best fit of p i ( z ) to all the trials carried out thus far. It then determines the value of z for which the most recent p ; ( z ) = 76% and selects the signal amplitude for the next

X /A Fig. 5. Psychometric curve.

TABLE IV SUBJECT DATA

Subject Age Flying Simulator No. (years) Time (h) Time (h)

1 24 220 0 2 25 33 0 3 28 2 50 4 29 155 100 5 28 50 10 6 25 111 0

303

3

trial to be near z. It was found that 30 trials were sufficient to home in on a steady result, taken to be the final estimate of z for pi(z) = 76%.

VI. SUBJECTS

Six males served as paid subjects. Their ages ranged from 24 to 29 years. None of the subjects reported any health problems or problems with their vestibular system. Their particulars are contained in Table IV.

VII. EXPERIMENTAL PROCEDURE

The experiment consisted of the determination of critical amplitudes for onset and return motions using test signals of the type shown in Fig. 4.

The onset motion tests were carried out using motion signals having ~ , i ~ = 0. Fig. 6 shows a typical example of an onset test signal. The value of T,,, for each forced choice trial was determined by the adaptive algorithm described previously. All onset motions were to the right and the subjects were aware of this.

The return motion tests employed the full test signal of Fig. 4 with a fixed value of T,, = 6.5 de@. This onset motion was easily detected. In each forced choice trial, the interval without the test signal had an onset signal as shown in Fig. 6 with rmax = 6.5 de& to match the onset portion of the test signal. The value of r,in for each forced choice trial was determined by the adaptive algorithm described previously. All

304 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. 22, NO. 2, MARCWAPRIL 1992

SI s2S3WS5S6

I I 1 I

0 5 10 TIME (SI

Fig. 6. Onset motion test signal

TASK I I

Fig. 8. Onset motion critical amplitudes.

from this test the following assumptions about the significance of the results have been made. Here P(z > F ) represents the probability that an F ratio value as large as that found could have occurred by chance given that the factor under consideration has no effect on the measured performance:

P(z > F ) Significance 0-0.01 very significant effect

I I I 40 80

TIME (SI Fig. 7. Return motion trial: Task A.

onset motions were to the right and the subjects were aware of this.

Each subject received from 20 to 60 min of practice prior to each run on the day of the run. Only one of the six runs (3 tasks x 2 conditions (onset and return tests)} was performed by a subject on a given day. Each run consisted of 30 forced choice trials and took from 45 to 60 min including breaks. A long break of approximately 10 to 15 min was taken after 14 trials and a short break of 5 min was taken after 25 trials.

The experiment involved 36 runs of 30 trials each. Each of the 6 subjects was tested once on each of the 6 conditions {(Tasks A, B , and C ) x (onset and return motion)}. The 6 conditions were performed in a fully randomized order.

A typical yaw velocity time history for a Task A return motion trial is shown in Fig. 7. Both the test signal and the random background motion can be seen. Interval 1 contains the pure onset motion signal while interval 2 contains the complete test signal.

VIII. RESULTS AND ANALYSIS

All of the critical amplitudes found in the onset motion tests are shown in Fig. 8. The corresponding results for the return motion tests are shown in Fig. 9.

An analysis of variance was carried out on these data (after first converting them to a decibel scale) based on a three factor mixed-effect model following [16]. In assessing the results

0.01-0.05 significant effect 0.05-0.10 mildly significant effect

>0.10 no significant effect. The results from the analysis of variance are contained in

Table V. Here “signal” refers to the onset and return motion test conditions. It can be seen that the task condition produced a very significant effect while subjects and signal effects were not significant. This tends to indicate that the test subject group was homogeneous and that the critical amplitudes for the detection of onset and return motions were not significantly different. These trends are apparent in Figs. 8 and 9. In Fig. 10 and Table VI it is seen that based on data averaged over all six subjects, Task A stands out as having significantly lower critical amplitudes than Tasks B and C. This is in agreement with the findings of [3] and it is assumed that the tracking activity associated with Tasks B and C prevents the pilot from directing his full attention to the motion detection process as is possible during Task A. The subjects reported that they found Task B to be more difficult than Task C and this may account for the increased critical amplitude for return motion found for Task B when compared with Task C.

It is interesting to note that the detection levels reported by other authors for yaw motion (see Table 111) obtained under conditions comparable to Task A tend to be significantly larger than the critical amplitudes found in the present study. This may indicate that the motion transients employed herein are more easily detected than the waveforms employed in past studies. Other potential differences could arise from the experimental apparatus, experimental procedures and detection criteria.

The linear acceleration at the pilot’s head as given by ( 5 ) and (6) can stimulate his otolith system and aid in the detection of motion. From Fig. 4 it is seen that the onset motion velocity

SAMJI AND REID: DETECTION OF LOW AMPLITUDE YAWING MOTION TRANSIENTS IN A FLIGHT SIMULATOR 305

4

3

2

(deg/s)

SI s2S3S4S5S6

’+I 00 0

‘ A B C TASK

Fig. 9. Return motion critical amplitudes.

x -Onset o-Return

(deg/s)

1 X

0 I

0

0 X X

1 A B C

TASK

0

Fig. 10. Average critical amplitudes.

peak T,, is preceded by the acceleration peak tmaX. Thus, in detecting onset motion, the peak linear acceleration that could be used by the pilot to augment the angular motion sensation would come from i,,, and rmax. From [14] it is known that for the current test signals

f,,, = 5.4Tmax. (9)

Based on the critical amplitudes for onset motion shown in Fig. 8 and (4H7) and (9) it is found that only the a: contribution from tmaX (with values in the range 0.018 to 0.071 m/s2) exceeds the detection levels reported in the Introduction.

In the case of detecting return motion by sensing linear acceleration it can be seen from Fig. 4 that the largest contribution would be from i,,, and r,in. From [14] it is known that for the current test signals +,in = 3 deg./s2. Based on this and the critical amplitudes for return motion shown in Fig. 9 and (4) to (7) it is found that only the a: contribution from +,in (with a fixed value of 0.024 m/s2) exceeds the detection levels reported in the Introduction.

These linear accelerations could help to reduce the critical amplitudes for yaw motion found in the present study. In addition, the larger linear accelerations associated with the onset motion measurements for Tasks B and C could explain their lower average critical amplitudes relative to the return motion values shown in Fig. 8.

TABLE V ANALYSIS OF VARIANCE

Degrees of Sum of Mean F Effect Freedom Squares Square Ratio P(x>fi Task 2 323.61 161.81 12.56 0.0019

1 1.28 1.28 0.0992 0.7592 Signal Task x Sig 2 61.33 30.67 2.38 0.1427 Subject 5 104.57 20.91 1.62 0.2404 Task x Sub 10 193.24 19.32 1.50 0.2666 Sub x Sig 5 83.03 16.60 1.29 0.3416

T x Sig x Sub Total 35 895.91

10 128.84 12.88

TABLE VI AVERAGE CRITICAL AMPLITUDES (degis)

Task ~~

A B C

Onset 0.73 1.10 1.10 Return 0.48 1.76 1.25

IX. CONCLUSION

These conclusions apply to the detection of transient yawing motions of the type depicted in Fig. 4 applied about the z-axis of a typical modern flight simulator.

1) The only significant factor influencing critical amplitude values in the present experiment was the task performed by the pilot in parallel with the motion detection process. The addition of a flying task increased the critical amplitudes for both onset and return motion detection.

2 ) The critical amplitudes for the detection of yawing motion found in the present study were significantly lower than detection levels reported by other researchers and listed in Table 111. The main reason for this is thought to be the motion waveforms employed and the presence of otolith stimulation.

A. Recommendations for Further Work

Based on the present findings it appears that follow-on studies would be useful as a means of creating a more complete data base for the simulator designer. These studies should:

1) employ pilot offsets from the axis of rotation ranging from 0 to 2 m in order to cover the practical range of semicircular canal to otolith stimulation,

2) extend the work to include the other 5 degrees-of- freedom of motion,

3) examine pilots’ sensitivity to other common transients present during maneuvers in a flight simulator,

4) study the effect of onset motion magnitude on the perception of return motion.

REFERENCES

[ l ] G. L. Greig, “Masking of motion cues by random motion: Comparison of human performance with a signal detection model,” UTIAS Rep. 313, Univ. Toronto, ON, Canada, Jan. 1988.

[2] A. J. Gundry, “Thresholds to roll motion in a flight simulator,” J. Aircrufi, vol. 14, no. 7, pp. 624431, 1977.

306 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. 22, NO. 2, MARCWMRIL 1992

[3] R. J. A. W. Hosman and J. C. van der Vaart, “Vestibular models and thresholds of motion perception: Results of tests in a flight simulator,” Delft Univ. Technology, The Netherlands, Rep. LR-265, Apr. 1978.

[4] M. Kirkpatrick and R. G. Brye, “Man-systems evaluation of moving base vehicle simulation motion cues,” NASA CR-120706, Apr. 1974.

[5] J. L. Meiry, “The vestibular system and human dynamic space orienta- tion,’’ NASA CR-628, Oct. 1966.

[6] J. Huang and L. R. Young, “Sensation of rotation about a vertical axis with a fixed visual field in different illuminations and in the dark,” Exp. Brain Res., vol. 41, pp. 172-183, 1981.

[7] M. Rodenburg, H. P. W. Stassen, and A. J. J. Maas, “The threshold of perception of angular acceleration as a function of duration,” Biological Cybern., vol. 39, pp. 223-226, 1981.

[8] G. L. Zacharias, “Motion cue models for pilot-vehicle analysis,” aerospace medical research laboratory, AMRL TR-78-2, Wright Patterson AFB, OH, Mar. 1978.

[9] L. D. Reid and M. A. Nahon, “Flight simulator motion-base drive algorithms: Part 1-Developing and testing the equations,” UTIAS Rep. 296, Univ. Toronto, ON, Canada, Dec. 1985.

[lo] L. D. Reid and M. A. Nahon, “Flight simulator motion-base drive algorithms: Part 2-Selecting the system parameters,” UTIAS Rep. 307, Univ. Toronto, ON, Canada, May 1986.- L. D. Reid and M. A. Nahon, “Flight simulator motion-base drive algorithms: Part 3-Pilot evaluations,” UTIAS Rep. 319, Univ. Toronto, ON, Canada, Dec. 1986. B. Etkin, Dynamics of Atmospheric Flight. R. V. Parrish, “Experiments in sensing transient rotational acceleration cues on a flight simulator,” NASA Tech. Rep. 1537, Oct. 1979. A. Samji, “Motion perception studies for the roll and yaw axes with the pilot in the loop,” Univ. Toronto, Institute for Aerospace Studies, ON, Canada, M.A.Sc. Thesis, Apr. 1990. P. R. Grant, “Motion characteristics of the UTIAS flight research simulator motion-base,” UTIAS TN no. 261, Univ. Toronto, ON, Canada, July 1986. B. J. Winer, Statistical Principles in Experimental Design. New York: McGraw Hill, 1971.

New York: Wiley, 1972.

Dr. Reid is an Assoc He recently served as Mechanics Panel.

AI-amyn Samji received the B.Math degree in applied mathematics from the University of Water- loo, Waterloo, ON, Canada, and the Ma.Sc. degree in Aerospace Engineering from the University of Toronto, Toronto, ON, Canada.

He is a member of the Systems Operations group at Spar Aerospace Limited. His current interests are robotic simulation and operations analysis, trajec- tory planning, and robotic kinematics.

Lloyd Reid received the Ph.D. degree in aerospace engineering from the University of Toronto, Toronto, ON, Canada.

Since 1969 he has been a professor of aerospace engineering at the University of Toronto Institute for Aerospace Studies where he is currently an Associate Director. He has published more than 40 technical papers in various journals and conference proceedings. His interests include flight mechanics, flight simulation, expert systems and aircraft stabil- ity and control.

:iate Fellow of the AIAA and a Fellow of the CASI. a Canadian member of the NATO AGARD Flight