NASA Contractor Report 3312 

Pilot/Vehicle 

of Visual and 

Requirements 

Model Analysis 

Motion Cue 

in Flight Simulation 

Sheldon Baron, Roy Lancraft, 

and Greg Zacharias 

CONTRACT NASZ-10145 

OCTOBER 1980 
NASA Contractor Report 3312 

TECH LIBRARY KAFB, NM 

Pilot/Vehicle Model Analysis 

of Visual and Motion Cue 

Requirements in Flight Simulation 

Sheldon Baron, Roy Lancraft, 

and Greg Zacharias 

Bolt, Beranek and Newman, Iztc. 

Cam bridge, Massachusetts 

Prepared for 

Ames Research Center 

under Contract NAS2-10145 

NASA 

National Aeronautics 

and Space Administration 

Scientific and Technical 

Information Branch 

1980 
1. 

2. 

INTRODUCTION . . . . . . . . . . 

OPTIMAL CONTROL MODEL . . - . . . . 

2.1 General System Description- . . . 

2.2 Overview of the Pilot Model . . . 

3. VISUAL AND VESTIBULAR PERCEPTUAL MODELS . 

3.1 Visual Perception Model. . . . . 

3.2 Vestibular Perception Model . . . 

3.3 Attention-Sharing Model. . . . . 

4. TASK/SYSTEM DESCRIPTION . . . . . . 

4.1 Description of Flight Task. . . . 

4.2 Simulator Configuration. . . . - 

5. 

6. 

7. 

4.3 Model Implementation of Simulator Characteristics . 

CLOSED-LOOP ANALYSIS OF CGI AND VMS EFFECTS. .... 

5.1 Simulator Configurations ......... 

5.2 Pilot Model Parameters .......... 

5.3 Results and Discussion .......... 

SUMMARY AND CONCLUSIONS ........... 

REFERJ3JCES ............... 

TABLE OF CONTENTS 

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APPENDIX A: SYSTEM MATRICES FOR THE SIMULATED CH-47 HOVER 

TASK . . . . . . . . . . . . . . 

APPENDIX B: DESIGN OF SIMULATOR CONTROL LAWS . . . . . 

B.l Overview. . . . . . . . . . . . . . . 

B.2 

B.3 

Description of the Design Procedure. . . . . . 

Design of Pitch-Surge Control Law . . . . . . 

. 2 

. 5 

. 7 

. 11 

. 22 

22 

: 44 

. 51 

. 54 

54 

: 57 

. 59 

I 73 

. 73 

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‘7 Il. 

. 72 

.lll 

.114 

. 119 

. 144 

.144 

.146 

-156 

-i- 
LIST OF FIGURES 

2.la 

2.. lb 

2.2 

3.1 

3.2 

3.3 

4.1 

4.2 

4.3 

4.4 

5.la 

5.lb 

5.2a 

5.2b 

5.3a 

5.3b 

5.4a 

5.4b 

5.5a 

5.5b 

5.6a 

5.6b 

5.6~ 

5.6d 

5.6e 

5.6f 

5.6g 

5.6h 

5.7a 

5.7b 

Closed-Loop Helicopter Hover Task. . . . 

Simulated Helicopter Hover Task. - - - - 

Overall Pilot/Vehicle System - - - - l 

Line-of-Sight Changes Due to Vehicle/Pilot 

Rotations . . . . . . . . . . . . 

Line-of-Sight Changes Due to Vehicle/Pilot 

Translations - - - - - - - - - - - 

Vestibular Model. . . . . . . . . . 

CGI Screen Configuration - - - - - - - 

VMS Model . . . . . . . . . . . . 

Simulator Mappings for OCM Analysis - - - 

Visual and Motion Path Delays . . . . . 

Relative Performance Vs. Simulator Configuration 

(CH-47 Longitudinal Axis)'. . . . . . . . . 

Relative Performance Vs. Simulator Configuration 

(CH-47 Lateral Axis). . . . . . . . . . 

Effect of Workload (CH-47 Longitudinal Axis) - - . 

Effect of-Workload (CH-47 Lateral Axis) . . . . 

Relative Performance Vs. Time Delay (Longitudinal 

Axis-Moving Base) - - - - . - - - - - - 

Relative Performance Vs. Time Delay (Lateral 

Axis - Moving Base)- - - - - - - - - - - 

Relative Performance Vs. Time Delay (Longitudinal 

Axis - Fixed Base . . l a l a m m a - m 

Relative Performance Vs. Time Delay (Lateral 

Axis - Fixed Base) . . . . . . . . . . . 

Relative Performance Vs. Field of View and Displav 

Resolution (CH-47 Longitudinal Axis) . . . . . 

Relative Performance Vs. Field of View and Display 

Resolution (CH-47 Lateral Axis). . . . . . . 

Relative Performance Vs. X Threshold . . . - - 

Relative Performance Vs. k Threshold . . . . . 

Relative Performance Vs. Z Threshold . 

Relative Performance Vs. 2 Threshold . 1 : : : 

Relative Performance Vs. 8 and 6 Thresholds. . . 

Relative Performance Vs. .@I and # Thresholds. . . 

Relative Performance Vs. Y and y Thresholds. . . 

Relative Performance Vs. Q and $ Thresholds. . . 

Relative Performance Vs. Observation Noise Ratio 

(CH-47 Longitudinal Axis). . . . . . . . . 

Relative Performance Vs. Observation Noise Ratio 

Page 

6 

6 

8 

31 

32 

45 

60 

61 

63 

65 

81 

82 

2- 

90 

90 

91 

91 

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100 

100 

102 

(CH-47 Lateral Axis) . . . . . . . . . . 103 

-ii.. 
5.8 

5.9 

5.10 

A.1 

A. 2 

B-1 

B.2 

3.1 

3.2 

3.3 

3.4 

4.1 

4.2 

4.3 

5.1 

5.2 

5.3 

5.4 

5.5 

5.6 

5.7 

LIST OF FIGURES (Cont.) Page 

Relative Performance Vs. VMS Platform Bandwidth. .105 

Relative Performance Vs. Control Augmentation . 

.109 

Performance Ratio Vs. Simulator Configuration . .llO 

Linking Diagram for Nominal Simulator Configuration 

(Longitudinal Axis) . . . . . . . . . . .12O 

Linking Diagram for Nominal Simulator Configuration 

(Lateral Axis). . . . . . . . . .' . . 

Structure of Fixed-Form Control Laws . . . . 

Design of Control-Law Design Problem . . . . 

.121 

.145 

.149 

LIST OF TABLES - 

Geometric Visual Cues . . . . . . . . . 

Visual Scene Informational Threshold Functions . 

Parameter Values for Vestibular Perception Model 

SimplifiCatiOnS to the Vest$~ular Model . . . 

Nominal CGI Characteristics . . . . . . . 

VMS Model Parameters. . . . . . . . . . 

Visual Scene Informational Threshold Values. . 

Realistic (Nominal) SiIIKilatOr COnfigUratiOn. . 

Simulator Configurations . . . . . . . 

Nominal Pilot Model. . . . . . . . : . 

Attention Allocation for Nominal Configurations. 

RMS Performance Scores. . . . . . . . . 

Simulator Workload Penalties . . . . . . . 

Effect of Surge Washout on RMS Platform Motion 

(CH-47 Longitudinal Axis) . . . . . . . . 

. 24 

43 

: 49 

51 

: 59 

61 

: 72 

75 

: 76 

77 

: 80 

84 

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-107 

-iii- 
ACKNOWLEDGEMENT 

The work reported here was performed under NASA Ames Research 

Center Contract Number NAS2-10145. The authors wish to thank Mr. 

Frank Crane, technical monitor for NASA, ,for his assistance 

throughout the course of the program, and Dr. William H. Levison of 

BBN, for his help in the washout design application, and his 

authorship of Appendix B of this report. 

-l- 
1. INTRODUCTION 

As flight control and management tasks become more complex so, 

too, do the simulators used to investigate these tasks. The 

designers of simulations are confronted with difficult choices 

between requirements for simulation fidelity and the needs for 

cost-effective methods of simulation. The latter demands have 

resulted in a trend toward the use of digital equipment in 

simulation both in modeling the vehicle and in generating visual 

cues (CGI systems) for the pilot of the simulator. These digital 

simulations can have characteristics that are significantly 

different from those desired. In particular, unwanted delays 

frequently result in such a simulation. When motion cues are also 

needed, the problems can be aggravated further both by delays in 

generating motion cues (even with analog hardware) and by the 

potential lack of correlation between visual and motion cues. The 

significance of these problems has been amply demonstrated in 

recent studies (Gum and Albery (1977), Queijo and Riley (1975)). 

Unfortunately, the specification of fidelity requirements for 

the generation of visual and motion cues is very difficult for 

several reasons. The requirements are governed by the purpose of 

the simulation: training simulators have different needs than 

research simulators. They are also problem dependent (e.g., the 

need for motion cues in the analysis of aircraft control in a gusty 

-2- 
environment will depend on the gust response of the aircraft.) 

Finally, it is difficult because it involves complex psychological 

as well as engineering factors. Not only is there a need to 

understand and account for the perceptual capabilities of a human 

operator, but also one must account for the fact that the adaptive 

human pilot may be able to compensate for simulator shortcomings 

and maintain system performance at the expense of workload 

(assuming the real cues are helpful, an assumption that is not 

always valid). In such a situation, the pilot could give a 

degraded evaluation of the system that would be unwarranted. 

"Rules of thumb", open loop response measurements, and 

subjective feedback from pilots are all helpful in developing the 

cue requirements for simulators. For simulations in which the 

pilot's principal task is flight control, a pilot/vehicle model 

that accounts for perceptual limitations of the pilot/environment 

interface can be very helpful in defining the cue generation 

requirements. An appropriate model can provide the designer with a 

too1 to examine potential performance penalties and 

performance/workload tradeoffs in the mission context of interest. 

Thus, it can aid the designer greatly in defining the requirements 

for the particular simulation of interest. 

In this report, such a pilot/vehicle model is used to 

investigate the closed-loop consequences of the performance 

-3- 
limitations associated with a computer generated image (CGI) visual 

system and a six degree-of-freedom motion simulator (VMS) in a 

helicopter hover task. In particular, the performance/workload 

effects of these simulation elements are analyzed using the optimal 

control model (004) for the pilot/vehicle system. (Baron and 

Kleinman (1968), Kleinman et al (1970, 1971)). To accomplish this, 

the basic OCM is elaborated to include sensory perception of both 

CGI-generated external visual cues and VMS-generated motion cues. 

-4- 
2. OPTIM&L CONTROL MODEL 

The closed-loop helicopter hover task which was the object of 

this study is illustrated in block diagram form in figure 2.la. 

The pilot's task is to hover over a fixed point at a fixed 

altitude, in the presence of disturbances generated by air 

turbulence. Control is to be maintained by relying on 

extra-cockpit visual cues obtained from an out-the-window view and 

by motion cues associated with helicopter rotation and translation. 

Figure 2.lb is a similar block diagram showing how the task is 

assumed to be implemented on the simulator. Visual cueing is 

provided by a computer generated image (CGI) system, and motion 

cueing is provided by a vertical motion simulator (VMS).* The 

problem addressed in this study was to determine the potential 

effects of CGI and VMS system characteristics on closed-loop hover 

performance and pilot workload, and to evaluate these effects in 

light of performance/workload levels we might expect to see in the 

actual flight situation. 

Our objective in this chapter is to describe how this task is 

modelled in the context of the Optimal Control Model (OCM) of the 

pilot. Inasmuch as the model has been documented extensively 

‘ZInFspite-ST-its name, 

--- the VMS is not-restricted to vertical 

motion cues; it is a six degree-of-freedom cueing system. 

-5- 
r DISTURBANCE 

+ 

. 

a CONTROL __) HELICOPTER DISPLAY 

- INTERFACE DYNAMICS d INTERFACE 

- 

CONTROLS DISPLAYS 41 PI LOT 0 

Figure 2.la: Closed-Loop Helicopter Hover Task 

s 

SIMULATED 

DISTURBANCE 

1 I 

* - CGI ’ 

SIMULATED 

CONTROL w HELICOPTER -1 ; 

SIMULATED I 

Z I 

INTERFACE DYNAMICS I 1 

3 VMS 

I I 

I ----I 

CONTROLS 

c I 

MOTION CUES A -u 

PI LOT 

I 

Figure 2.lb: Simulated Helicopter Hover Task 

-6- 
(Baron and Kleinman (1968), Kleinman et al (1970, 1971)), the 

discussion will be brief, with emphasis on those aspects of the 

model that are of special relevance to this study. First, however, 

we will give a general description of the system under study, in 

section 2.1, to provide a background for the pilot model 

description given in section 2.2. We will defer a detailed 

description of the "perceptual" portion of this model to chapter 3; 

likewise, we will defer a complete specification of the task and 

system dynamics to chapter 4. 

2.1 General System Description _--. 

A detailed block diagram of both the system and the OCM pilot 

model is given in figure 2.2. The system portion (outside the 

dashed box) provides for representations of control stick dynamics, 

vehicle dynamics, and the dynamics associated with the simulator 

drive logic (e.g., a motion base washout filter) and its hardware 

(e.g., the VMS servo drives). As shown, the two inputs to the 

system are the set of controls generated by the pilot (u), and the 

system disturbances which perturb the vehicle dynamics (a). The 

set of system outputs is the cue set provided by the simulator to 

the pilot's various sensory systems.* 

* Although an instrument "channel" is shown in the diagram for 

generality, the present study presumed that no such informational 

cues were available to the pilot. 

-7- 
I 

CJ 

I 

r------ - - ._ ._--- 

1 

OBSERVATION ’ 

DISTURBANCE --Id 

SENSORY 

SIMULATOR SYSTEMS 

VECTOR, 1 

CONTROL 

FORCE 

A- r-----Y; - 

STICK 2 VEHICLE SIMULATOR 

TRANSDUCER DYNAMICS DRIVE 

L 7 9 

INSTRUMENTS & r “0s 

------___ VISUAL --G 

LOGIC EXTRA-COCKPIT I N 

AC, I I 

DISPLAY 

.---- ----_ Y 

-a 1. -+ MOTION BASE 

I 

-------_-_-- _______ ---------------I 

MOTOR 

DYNAMICS 

MOTOR NOISE CONTROL GAINS OBSERVATION NOISE 

L----------- \ ---- PILOT ----- - --.- ----- --------- -.----A$ 

MODEL 

Figure 2.2: Overall Pilot/Vehicle System 
As we noted earlier, the detailed descriptions of these 

subsystems will be given later in chapter 4. Here, however, it 

suffices to note that our system modelling approach involves: a) a 

linearization of the relevant dynamics associated with each of the 

subsystems; and b), the construction of a state-variable 

representation of the combined system dynamics. The resulting 

vector- matrix state equation has the following form: 

g(t) = Ax(t) + Bu(t) + Ew(t) - - - (2.1) 

where x(t) is the n-vector which describes the state of the 

simulator system, u(t) the r-vector of pilot control inputs, and - 

w(t) a vector of white driving noise processes, the latter included 

to model the system disturbances. In general, the matrices A, B_, 

and E may all be time-varying (piece-wise constant) to reflect 

changes due to differing flight conditions; in our application, 

however, they are fixed for a specific hover condition and vehicle 

configuration. 

As noted, the above system model includes all of the dynamics 

associated with all of the subsystems comprising the simulator. In 

general, however, the system model will include additional dynamics 

associated with three other aspects of the closed-loop control 

task: a) the disturbance or gust model; b) the dynamics which 

characterize the pilot's sensory capabilities; and c) any dynamics 

-9- 
which might be used to approximate other system characteristics 

which cannot be expressed directly in terms of linear first-order 

vector-matrix equations. We discuss these points in the following 

paragraphs. 

Insofar as gust models can be represented by rational noise 

spectra, they can be incorporated in the system model by first 

determining the appropriate shaping filter, which, when acting on 

white noise, generates the desired gust spectrum. By expressing 

this shaping filter in state-variable format, the system (2,l) may 

then be augmented to generate appropriate gust states which are 

driven by the white noise process vector x(t), through the 

disturbance input matrix E_. 

If the pilot's sensory dynamics are deemed relevant to 

understanding closed-loop performance in the given task, the 

dynamics may be expressed in state variable form, and used to 

augment the system dynamics of (2.1). We will discuss this at 

greater length in the next section, in our description of the pilot 

portion of the closed-loop system. 

System dynamics which, after linearization, are not directly 

expressible in the form of (2.1) may be included in the system 

description by first finding a suitable state-variable 

approximation and then augmenting (2.1) with this approximation. 

-lo-. 
Pure time delays, in particular, are conveniently handled by this 

approach. An appropriate Pade filter approximation is found, and 

the associated state variable dynamics are used to augment the 

system dynamics of (2.1). 

In summary, the system (2.1) not only includes the explicit 

dynamics of the various simulator subsystems, but also the implicit 

dynamics associated with the disturbance spectra, the relevant 

sensory dynamics of the pilot, and any additional approximations 

deemed necessary for accurate system modelling. 

2.2 Overview of the Pilot Model -- 

The basic assumption underlying the optimal control model for 

the pilot is that the well-trained , well-motivated human controller 

will act in a near optimal manner subject to certain internal 

constraints that limit the range of his behavior and also subject 

to the extent to which he understands the objectives of the task. 

When this assumption is incorporated in the optimal control 

framework and when appropriate limitations on the human are 

imposed, the structure shown within the dashed lines of of Figure 

2.2 results. In discussing this structure it is convenient and 

meaningful to view this model as being comprised of the following: 

(i) a display interface which converts system state variables and 

pilot control outputs into a set of "displayed" variables y(t); 

-ll- 
(ii) an "equivalent" perceptual model that translates these 

variables into noisy, delayed "perceived" variables denoted by 

yp(t) ; (iii) an information processor, consisting of an optimal 

(Kalman) estimator and predictor. that generates the 

minimum-variance estimate E(t) of x-(t); (iv) a set of "optimal 

gains", &*, chosen to minimize a quadratic cost functional that 

expresses task requirements; and (v) an equivalent "motor" or 

output model that accounts for "bandwidth" limitations (frequently 

associated with neuromotor dynamics) of the human and his inability 

to generate noisefree controls. We now discuss these model 

components in greater detail. 

2.2.1 Display Interface 

The display interface provides a means for transforming the 

system state variables and the pilot's control actions into a 

display "vector" which represents that set of all information 

available to the pilot. The components of the display vector are 

assumed to be linear combinations of the state and control' 

variables, and are defined by the following m-dimensional vector 

equation: 

y(t) = Cx(t) + Du(t) - - (2.2) 

where C_ and D may be time-varying (piece-wise constant) to account - 

for changes in the quantities being displayed or "observed". 

-12-- 
In the present task, we assume that the only information 

available to the pilot is that which he obtains via his visual and 

vestibular sensory systems. As shown in the figure then, the 

display vector y can be partitioned as follows: 

(2.3) 

where yvis and yves are the outputs of the two sensory systems. In 

the more general situation, the display vector could include 

"displays" from other sensory modalities, such as proprioceptive, 

tactile, or auditory cues. In addition, the individual displav 

vectors associated with a particular modality (e.g., Yvisf yves, 

etc.) can be expanded to account for information provided by other 

cueing systems which impinge on that same modality. Thus, in the 

current study, we have assumed that yvis reflects the pilot's 

reliance on only out-the-window (visual) cues; if he were to have 

available additional instrument (visual) cues, we would augment 

Y vis to account for the information provided by this additional 

(same-modality) cueing system. 

In general, the processing provided by the pilot's sensory 

systems requires a model which involves not only a linear 

transformation of the system state (as in (2.2)), but also a 

dynamic transformation which accounts for any important sensory 

processing dynamics (e.g., vestibular dynamics). As we noted 

-13- 
earlier, this latter modelling requirement is implemented by 

assigning the sensory dynamics to the set of overall system 

dynamics, and appropriately augmenting the state equation of (2.1). 

2.2.2 Perceptual Model 

Limitations on the pilot's ability to process information 

"displayed" to him are accounted for in the "equivalent" perceptual 

model. This model translates the displayed variables y into 

delayed, "noisy" perceived variables yp via the relation 

yp(t) = y(t’T) + v_y(t-T) (2.4) 

where T is an "equivalent" perceptual delay and xy is an 

"equivalent" observation noise vector.* 

The various internal time delays associated with visual, 

vestibular, central processing and neuro-motor pathways are 

combined and conveniently represented by this lumped equivalent 

perceptual time delay T . Typical values for .this delay are 

0.2 + .05 sec. (Kleinman et al (1971)). 

* The use of the word equivalent in this context is to emphasize 

that the parameters may be lumped representations of a variety of 

limitations that can not be "identified" separately by existing 

measurement techniques. 
- 

The observation noise xy lS included to account for the 

pilot's inherent randomness, due to random perturbations in human 

response characteristics , errors in observing displayed variables, 

and attention-sharing effects which limit the pilot's ability to 

accurately process all the cues simultaneously available to him. 

In combination with the motor noise model (described below in 

section 2.2.4), the observation noise model provides a convenient 

and accurate means of modelling pilot remnant and thus accounting 

for random control actions. 

For manual control situations in which the displayed signal is 

large enough to negate the effects of visual resolution 

("threshold") limitations, the autocovariance of each observation 

noise component appears to vary proportionally with mean-squared 

signal level. In this situation, the autocovariance may be 

represented as 

2 

vi(t) = *'jcly (t) 

i 

(2.5) 

where c 2 . 

yi ls the variance of the ith output, Pi is the 

"noise/signal ratio" for the i th display variable, and has units of 

normalized power per rad/sec. Numerical values for Pi of 0.01 

(i.e., -20 dB) have been found to be typical of single-variable 

control situations (Levison et al (1969), Kleinman et al (1970)). 

-15- 
The perceptual model defined by (2.4) and (2.5) applies to 

"ideal" display conditions, in which the signal levels are large 

with respect to both system-imposed and pilot-associated 

thresholds. To account for threshold effects we let the 

autocovariance for each observation noise process be 

vi(t) = Pi 

(2.6) 

where the subscript i refers to the i th display variable. The 

quantity K(Ui,ai) is the describing function gain associated with a 

threshold device 

K(5, a) = 

+I-& e-x2 dx 

(2.7) 

- 

where "a" is the thres:ii;-C and c 1s the standard deviation of the 

"input" to the threshold device.* The net result of this type of 

describing function model is to increase the observation noise 

covariance as the 

to the threshold. 

The sources 

display signal variance becomes smaller relative 

of these threshold effects depend on the 

particular task being modelled. They may be associated with the 

system display implementation, for example, due to resolution 

limitations on a display screen. Or, they may be associated with 

------ -- * For non-zero mean sigas-t%is expressc= must be %drF%d (see 

(Baron and Levison (1973)). 

-lG- 
the pilot's sensory limitations, such as one might identify with 

visual acuity thresholds. In the hover task both types of 

threshold effects enter into the analysis, and will be discussed 

in greater detail in chapter 3. 

One additional factor which tends to increase the observation 

noise (present on any given display variable) is the pilot's 

attention-sharing limitations. Because the numerical valile 

associated with the pilot's noise/signal ratio (P) has been found 

to be relatively invariant with respect to system dynamics and 

display characteristics, we associate this parameter with 

limitations in the pilot's overall information-processing 

capability. This forms the basis for a model for pilot 

attention-sharing where the amount of attention paid to a 

particular display is reflected in the noise/signal ratio 

associated with information obtained from that display (Levison et 

al (1971) , Baron and Levison (1973)). Specifically, the effects of 

attention-sharing are represented as 

'i = Po/fi (2.8) 

where P i is the noise/signal ratio associated with the i th display. 

When attention is shared among two or more displays, fi is the 

fraction of attention allocated to the ith display, and PO is the 

noise/signal ratio associated with full attention to the task. A 

-17- 
detailed description of task-specific attention allocation levels 

is deferred to chapters 3 and 5, where we discuss the perceptual 

modelling and specific task components in more detail. 

2.2.3 Estimation and Control Models 

The optimal predictor, optimal estimator, and optimal gain 

matrix represent the set of "adjustments" or "adaptations" by which 

the pilot tries to optimize his behavior. The general expressions 

for these model elements are determined by system dynamics and task 

objectives according to well-defined mathematical rules that are 

derived in (Kleinman et al (1971)). The controller is assumed to 

adopt a response strategy to minimize a weighted sum of averaged 

output and control variances as expressed in the cost functional: 

J(u)= E[yT(t)Qyy(t) + u_TWQ+u_(t, + ~T(t)K$(t)l (2.9) 

where J(u) is conditioned on the perceived information yp.* 

The selection of the weightings Qy = diag [qy I, Q-U = diag 

[q,.] and 5 = diag [ri] in J(u) is a non-trivial St,', in applying 

1 

the OCM. The most commonly used method for selecting reasonable a 

priori estimates for the output weightings is to associate them 

with allowable deviations in the system variables, and has been 

described in several recent applications of the OCM (see, for 

----?- - -.7--7- *Thecost-functionaicanalsoinclude-.a term that 1s quadratic in 

the state. 

-18- 
example, Kleinman (1976)). The control related weightings may be 

chosen in a similar fashion or they may be picked to yield a 

desired value of rN, as discussed below. This method of choosing 

weightings has several advantages. Maximum or limiting values of 

system quantities are often easy to specify or elicit from pilots. 

In addition, with this normalization, the contribution of each term 

to the total cost depends on how close that quantity is to its 

maximum value; the penalty is relatively small when the variable 

is within limits but increases rapidly as the variable exceeds its 

limit. 

As noted above, the tandem of predictor and estimator generate 

a minimum variance estimate of the system state. As such, they 

(linearly) compensate for any time delays or noises introduced by 

the system and/or the operator. These elements incorporate 

"perfect" models of the simulation environment including models of 

the CGI and VMS systems.* Thus, the model predictions are 

appropriate for pilots that are well trained on the simulator. 

2.2.4 Motor Model 

Limitations on the pilot's ability to execute appropriate 

control actions are accounted for in the motor model, which is 

*deed they also 

limitations. 

XT?l%3%- models of the pilot's own sensory 

-19- 
composed of a white motor noise source and a first-order lag 

matrix. This model translates "commanded" controls, I&, into the 

output control actions u via the following relation: - 

XN4 + u = IJJ + v* (2.10) 

where TN is an "equivalent" lag matrix and & is an "equivalent" 

motor-noise vector. 

In laboratory tracking tasks with optimized control sticks, 

the motor lag parameters have been associated with the operator's 

neuro-motor time constant: accordingly, the lag values of the TN 

matrix have been set to a value of about 0.1 second. For more 

realistic flight control situations, however, this bandwidth 

limitation may be overshadowed by the system dynamics and flight 

control objectives, so that the 0.1 second value may lead to model 

predictions of control activity which exceed that observed in 

actual flight situations. In these cases, it is more reasonable to 

choose lag values by a model trade-off analysis in the following 

manner: choose a lag value such that if a larger value is chosen, 

substantial increases in tracking error will result, whereas if a 

smaller value is chosen, only marginal improvements in tracking 

performance will result. This method of choosing the motor lag at 

the " knee" of the cost vs. lag value curve was used for the study 

described here: the numerical results are given in section 4.1. 

-2O- 
The neuro-motor noise vector of (2.10) is provided to account 

for random errors in executing intended control movements, and, in 

addition, to account for the fact that the pilot may not have 

perfect knowledge of his own control activity. The motor noise is 

assumed to be a white noise, with autocovariance that scales with 

the control variance, i.e., 

vm.w = Pm/Ju2(t) 

1 11 

(2.11) 

Previous studies (Kleinman et al (1970)) have found, typically, 

that a value for Pm of .003 (i.e., a "motor noise ratio" of -25 dB) 

yields good agreement with experimental results. Throughout this 

study the motor-noise ratio was set to approximately -25 dB. 

This then concludes our general description of the pilot 

model. We now proceed to describe the perceptual portion of this 

model in more detail, since it is central to our evaluation of CGI 

and VMS cueing system specifications. 

-21- 
3. VISUAL AND VESTIBULAR PERCEPTUAL MODELS 

The previous chapter described the general characteristics of 

the perceptual portion of the optimal control model. Here, we 

provide more detail concerning the perceptual models for the two 

modalities considered most relevant to the hover task being 

studied: the out-the-window visual perception model, and the 

vestibular motion-sensing model. We also provide a brief 

discussion of the model for attention-sharing among the cues 

provided by these two modalities. 

3.1 Visual Perception Model -- ----~ 

In contrast to the relatively well-defined set of visual cues 

provided by within-cockpit instrumentation, the extra-cockpit 

visual scene can provide the pilot with an exceptionally rich 

stimulus environment, even for a relatively simple display. 

Attempting to describe and quantify this stimulus environment has 

been the object of many studies. For example, Brown (1973) 

discussed five "dimensions" of the visual world: field-of-view, 

range of luminance, color, spectral resolution, and visual motion. 

Staples (1970) listed fifteen factors that are present in the 

visual scene which can be of importance to the pilot, and noted 

that this list was incomplete. Gibson (1950,1955) concentrated on 

geometric and textural cues, but also noted the potential utility 
Of the "traditional" cues for depth perception (i.e., lens 

accommodation, binocular convergence, and retinal disparity). 

The literature on scene attributes is extensive, and it became 

clear to us that some narrowing of focus was called for, if a 

successful attempt was to be made in the area of modelling this 

type of cue processing. 

Matheny, et. al. (1971) present a taxonomy of cues that is 

helpful in structuring and limiting the problem. They define 

relevant cues as cues which are directly useful for controlling the - -- ---.-- 

aircraft or for making decisions. The non-relevant cues are those -e-F--- 

that are not essential to the successful operation of the aircraft, 

but which may add realism or face-validity to the task. Within the 

domain of relevant cues are subsets of primary, secondary, 

complementary and conflicting cues. This assumes that the operator 

has, for a given situation, a hierarchy of preferred cues and that 

he seeks a primary cue from a set of cues that are available. Cues 

that tend to reinforce his primary cues are called secondary if 

from the same modality, and complementary if from a different 

modality. Finally, cues that are in opposition in terms of the 

information they present are called conflicting (additional 

discussion can be found in Thielges and Matheny (1971)). 

-23- 
3.1.1 Visual Cueing Geometry 

It seems clear that in extending the optimal control model to 

account for perception of the visual scene, our first concern 

should be the relevant cues. Thus, for example, color can be 

neglected in our hover task, since adequate foreground-background 

separation can be provided by a sufficiently large contrast 

differential. Similarly, range of luminance might be neglected if 

adequate surface definition is provided by the display hardware. 

Other "non-geometric" visual cue factors can be similarly 

neglected, at least for an initial analysis, so that modeling can 

concentrate on the basic geometric characteristics of the visual 

scene. These were identified by Gibson (1950) in his analysis of 

visual scene processing: table 3.1 lists these "geometric" cues 

and the corresponding type of information they provide. 

Table 3.1: Geometric Visual Cues 

Cue Information -------1------ - 

Field Orientation Attitude, Attitude Rate 

Linear Perspective Position 

Motion Parallax Linear Velocity 

Apparent Size/ Position 

Size Constancy 

Occultation Position 

-24- 
Field orientation provides the pilot with attitude cues: 

assuming that the visual world is inertially fixed, then any 

rotation of the scene elements, measured with respect to the 

vehicle (or pilot) frame of reference , must be due to self-rotation 

of the vehicle (and pilot). Linear perspective changes with the 

location of the vehicle; thus, these cues provide the pilot with 

positional information. Motion parallax is effectively a rate of 

change of linear perspective, and thus provides the pilot cues for 

inferring linear self-velocity. The apparent size of an object 

(combined the pilot's perceptual set which ensures size constancy) 

provides a "looming" or range cue which can be used to infer 

relative distances, and hence position. Finally, occultation of 

one object by another provides an angular "line-of-position" cue 

similar to a navigation "fix", and, hence, also provides a means of 

inferring position. 

To illustrate how the use of these cues might be analyzed in a 

quantitative manner, we briefly summarize a study conducted by 

Wewerinke (1978). He concentrated on the pilot's use of field 

orientation, linear perspective, and motion parallax cues during a 

VFR landing approach. By use of elementary perspective geometry, 

he related rotational and translational vehicle movements to 

changes in orientation of line segments comprising the 

extra-cockpit visual scene. For example, he showed that small 

-25- 
changes in the perceived orientation of the runway centerline, with 

respect to the aircraft's longitudinal plane of symmetry, is given 

by 

w = Chh + Cyy + 4~ 

where w is the runway centerline deviation, Ch and C Y are 

trajectory dependent constants,* and h, y, and $ are small 

deviations in altitude, localizer error, and roll attitude, 

respectively. Similar expressions were derived for orientational 

changes in other line elements comprising the visual scene, and 

corresponding expressions were derived for their rates of change. 

To model pilot processing of these cues, Wewerinke (1978) 

followed the suggestion of Baron and Berliner (1975) and used the 

optimal control model and its "display vector" capability. 

Specifically, he recognized that each wi associated with a scene 

line element was linearly related to the vehicle state vector x, in 

the form 

T w. = c. x 1 -1 - 

(3.1) 

where c. is the "display gain" associated with the i th 

-1 line 

* Thi-<iGE> there are-ti6?cgn1ficant changes in vehicle position 

along the approach path, a condition which was satisfied in 

Wewerinke's study. 

-25- 
element. If the pilot's display vector y is composed of N 

extra-cockpit geometric cues, then 

y=g 

where 

c = k,, c,,...,c4JlT 

(3.2a) 

(3.2b) 

so that the display matrix C is completely defined for the purposes 

of model analysis. 

To test this hypothesis of visual cue processing, Wewerinke 

conducted a model analysis of experimental data obtained from a 

simulated VFR approach and found that the data could be closely 

matched by assuming: a) "nominal" pilot-related model parameters 

(except for a motor time constant of 0.25); b) optimal attention 

allocation between display vector elements (as described later in 

section 3.3; and c) individual display thresholds which were 

consistent with visual perception thresholds found in related 

psychophysical experiments. In short, the modeling approach toward 

visual cue processing was well-supported by the experimental data. 

Since a pilot's perspective of the runway is by far the most 

useful visual cue in a conventional VFR approach and landing, 

Wewerinke (1978) was able to use a highly schematized line drawing 

of the runway environment. As a consequence, there were a limited 

-27- 
number of well-defined cue elements comprising the visual scene, 

and thus the construction of the display matrix C was a relatively 

straightforward exercise. In the hover task, however, no single 

object is important. Instead, a pilot can use various portions of 

his visual field, and any number of objects or parts of objects to 

maintain his hover position and attitude. As a consequence, we 

have assumed that a relatively "realistic" visual scene is always 

available to the pilot. Since such a scene is typically comprised 

of thousands (or perhaps tens of thousands) of discriminable line 

elements (and hence cues), the display analysis outlined above is 

not appropriate here. 

Our approach, instead, was to take a much simplified view of 

visual cue processing, based on the foliowing notion. Each of the 

cues listed in table 3.1 involve changes in the location, length, 

and/or orientation of the various line elements comprising the 

visual scene. These changes, in turn, can be expressed in terms of 

changes to four parameters which specify the line element itself: 

the four angular coordinates associated with the line element 

endpoints, two coordinates per point. For our study, we have taken 

these two coordinates to be the azimuth and elevation angles 

associated with the line-of-sight (LOS) to the particular line 

element endpoint. 
Changes in these LOS angles are due to changes in vehicle 

state (position and attitude). Assuming small changes, we can use 

the linearized relation we introduced earlier, so that 

t-J vis = CT, -I8 = cTx vis -- e 

(3.3) 

whereJ'vis and evis are the azimuth and elevation LOS angles, % 

and --8 c are the display "gains", and it is understood that the above 

relation holds (with different gains) for each specific point in 

the visual scene. Thus, we would expect to have two such equation 

sets for each line element in the scene, with perhaps thousands of 

these set pairs associated with any given "realistic" scene. 

Naturally, all of these equations would be dependent on the 

specifics of the particular scene being analyzed. 

Rather than attempt to specify, and then solve, this large 

number of simultaneous equations in the state II, we chose to take 

an approach which effectively idealizes the scene content. We 

assumed, for each vehicle state the pilot was trying to estimate, - 

that the pilot chose one particular point in the visual scene to 

provide the most appropriate visual cue. Thus, if the vehicle 

state is comprised of three rotational coordinates and three 

translational coordinates, then, in general, there would be six 

specific points in the visual scene the pilot would use for 

inferring changes in vehicle state. 

-29- 
This is illustrated in figures 3.1 and 3.2, which show how 

specific vehicle rotations and translations result in changes in 

the azimuth and elevation angles associated with the line-of-sight 

to a specific point in the visual scene. 

Figure 3.1 shows the effect of vehicle (pilot) rotations. For 

both yaw (Q) and pitch (0) motion, we assume the presence of an 

object (or an identifiable part of an object) located along the 

forward (x) axis of the vehicle.* In yaw, this might be achieved 

through the presence of a sufficiently tall vertical reference 

(e.g., a tree or telephone pole); in pitch, via a sufficiently 

wide horizontal reference (e.g., a roof line or the horizon line). 

In either case, the figures show that the observed change in the 

LOS angle will be equal and opposite to the change in the 

corresponding vehicle attitude angle: 

G vis = -11, 

0 = vis -0 

(3.4a) 

(3.4b) 

Figure 3.1 also shows the effect of roll motion. We assume 

the presence of an object located on the horizon, and just within 

the lateral field-of-view of the pilot ($FOV). If small roll 

angles are assumed, then it is a direct matter to show that the 

*For-this-cueing anfix?; ---- 

we also assume the origin of the 

vehicle body axis to coincide with the location of the pilot's 

eyes. 

-3o- 
I 

W 

7 

Figure 3.1: Line-of-Sight Changes Due to Vehicle/Pilot 

Rotations 
GROUND PLANE 

evis= -(h/h,) cos 8, sin 8, 

: 

GROUND PLANE G 

; 

Figure 3.2: Line-of-Sight Changes Due to 

Vehicle/Pilot Translations 

-32- 
observed change in the LOS elevation angle is given by: 

8 vis = @(sin$JFov) 

Since the roll angle is assumed small, it is also a direct matter 

to show that the resulting change in the LOS azimuth angle (J/vis) 

is negligible. Thus, it can be neglected as a cue to inferring 

roll angle. 

Figure 3.2 shows the effect of vehicle (pilot) translations. 

For both surge (x) and heave (z) motion, we assume the presence of 

an object located in the vehicle's longitudinal plane of symmetry 

(the x-z plane), at ground level. This might be achieved through 

the presence of a small object ideallv located in the forward 

viewing plane, or via a horizontal reference line on the ground 

plane (e.g., a painted stripe on a helicopter landing pad). As 

shown in the figure, surge or heave motion results in a change in 

the LOS elevation angle. Assuming that the nominal hover altitude 

is ho, and that the nominal LOS depression angle* is eo, then it 

can be shown that 

(x-axis) 0 vis = -(x/ho)sin2eo (3.4d) 

--- ----~ rThe dep~~-~~~~~-~~~ negative of the elevation 

angle. 

'-33- 
(z-axis) evis = -(h/ho)cos0,sin8, (3.4e) 

where, consistent with our earlier assumptions, we assume small 

displacements x and h, relative to the sighted object distance 1,. 

Figure 3.2 also shows the effect of a lateral displacement 

along the vehicle y-axis We assume the presence of an object in 

the horizontal plane containing the vehicle origin (the x-y plane) 

and located off the longitudinal axis an angular distance$o. As 

in the yaw-axis case, this might be due to the presence of a 

sufficiently tall vertical reference such as a telephone pole or 

tree. Assuming a nominal object distance lo, it follows that the 

change in the LOS azimuth angle will be given by: 

4J vis = - (y/l,) cos o. (3.4f) 

where, as before, we assume that the vehicle translation y is small 

with respect to the sighted object distance lo. 

To this point, we have shown, for a given object in the visual 

scene, how the LOS azimuth and elevation angles will change due to 

vehicle motion. It should be clear that this is a many-on-two 

mapping, since quite different vehicle motions can result in 

identical changes in the LOS angle. For example, it can be shown 

from (3.4a) and (3.4f) that a yaw rotation $ and a lateral 

displacement y cannot be differentiated on the basis of a single 

LOS observation, if we choose 

-34 ‘- 
Y = l,~/cOsdJO 

where 1, andQo are as defined previously. However, it should also 

be clear that if we had two such observations, associated with two 

different objects in the visual field, then we would be able to 

differentiate between the motions. 

In the actual hover task, we have six distinct motions which 

must be differentiated: but with a "realistic" scene available, the 

pilot should have thousands of LOS "measurements" available to him. 

Thus, it seems safe to presume that the pilot can effectively 

"decouple" the many-on-two mapping , and correctly infer the vehicle 

motion from the given cue set of azimuth and elevation LOS angle 

changes. 

To avoid the necessity of postulating a cue decoupling model 

for the pilot, we have chosen to assume that the pilot can directly 

invert the equations given above, to correctly infer the vehicle 

motion from the observed LOS visual cue. For example, if the pilot 

observes an azimuth change (Avis) in the LOS to a specific object, 

and this has been caused by a y-axis translation, then we presume 

that he correctly infers this y-axis motion by inverting (3.4f): 

Y= - ( 1o/cos ~0’ Avis 

--35- 
where y is his inference from the observation Avis, and his 

knowledge of the nominal geometry (lo,Qo). 

Subject to the limitations described immediately below, this 

then is our basic model of visual cue processing. The azimuth and 

elevation LOS angles are the available cues. By choosing and 

inverting the appropriate equation in equation set (3.4), the 

correct vehicle motion can be inferred. It should be recognized 

that (3.4) presumes a specific cueing geometry (as described 

earlier) and thus is not cast in the most general form possible. 

However, it should be clear that any other cueing geometry choice 

will result in a similar set of six equations, which, under the 

modelling assumptions just given, could be similarly inverted to 

infer changes in vehicle attitude and position. 

3.1.2 Visual Thresholds 

It is now appropriate to consider the fact that the pilot will 

be limited in his ability to detect changes in the LOS angle cues 

available to him. This limitation will be due either to his own 

inherent sensory/perceptual limitations, or, in the simulator 

situation, possibly due to CGI-imposed resolution limits. The 

effective visual cue threshold will be the greater of the two 

thresholds associated with the pilot and the display hardware, and 

will ultimately limit the pilot's ability to infer vehicular state 

-35- 
changes from changes in the visual scene.* Naturally, if display 

hardware is not involved (as in the actual helicopter environment), 

then the effective threshold will be determined solely by the 

pilot's visual limitations. 

Turning first to the pilot's visual limitations, we make a 

distinction between angular resolution threshold (a,) and angular 

discrimination threshold (~1~). The former refers to his visual 

acuity, and his ability to resolve small angular differences in the 

LOS angle, when given a visual reference which, i. ‘7 anqular 

distance, is very close to the object being sighted. The latter 

refers to the pilot's ability to discriminate between two large 

visual angles, and thus his ability to identify a small angular 

difference i.n the LOS angle, when given a visual reference whi.ch, 

in angular distance, is relatively far from the object being 

sighted. 

The angular resolution threshold (a,) might be chosen on the 

basis of measured human visual acuity, which appears to be on the 

order of one minute of arc (Riggs (1965)). However, we chose to 

* Strictly -~5peaking,---'w~---~~o-uld '--.------ restrict our -ZCGiE~on to 

pilot-related visual thresholds in this section, since we are 

attempting to define the perceptual submodel of the pilot. 

However, since display-related visual thresholds will act in a 

functionally equivalent manner, it is appropriate to consider both 

types of limitations at the same time, and define an overall 

"effective" pilot/simulator visual threshold. 

-37- 
set it at a slightly higher level, based on an earlier analysis of 

the data obtained from dynamic tracking experiments (Levison 

(1971)): 

aR = 0.05 deg (3.5a) 

The angular discrimination threshold (a,) was chosen in accordance 

with the Weber-Flechner law (Lute and Galanter (1963)), and set at 

a fixed fraction of the total angle being viewed: 

CID = ao/30 

where c1 o is the total angle being viewed. 

We now define the pilot-associated visual threshold 

maximum of the resolution and discrimination thresholds: 

a = MAXtaRt%) 

(3.5b) 

as the 

(3.5c) 

To determine which of these two thresholds will dominate, we turn 

to the cueing geometry illustrated earlier in figures 3.1 and 3.2. 

Figure 3.1 shows that the nominal LOS angles for attitude cues is 

zero; thus aD is zero, and c1 is determined by the resolution 

thresholds. Figure 3.2 shows that the nominal LOS angle for a 

sway cue is Q,. If we specify this to be zero (i.e., a 

straight-ahead target), then the threshold associated with sway 

motion will also be determined by the resolution threshold. 

-33- 
Finally, if we assume reasonable values for hover height ho and 

object distance lo, say: 

hO = 10 ft. 

l0 = 50 ft. (3.6) 

then the LOS depression angle associated with a surge or heave cue 

will be on the order of 10 degrees. The discrimination threshold 

will therefore be on the order of 0.3 degrees. This determines the 

pilot-related threshold for these two types of visual motion cues. 

We now turn to the potential effects of CGI-imposed resolution 

thresholds. If the CGI has associated with it an average angular 

resolution 6, then the overall pilot/simulator visual threshold 

will be given by* 

Y = MAX(Q) (3.7) 

We will reserve discussion of particular values 0fBassociated 

with specific CGI configurations until Chapter 4, where we discuss 

the system characteristics in more detail. However, it suffices to 

note that f3 is on the order of the pilot's resolution threshold 

uR' Thus, if the pilot-associated threshold for a given cue is 

determined by the discrimination thresholdaD, then the overall 

---_----- ---- * Note thatwilT-- zero In the case of the "InfInIte resolution" 

display available from a true out-the-window scene in the actual 

helicopter. 

-33- 
pilot/system threshold will likewise be determined byaD. From the 

discussion given above, we therefore expect the overall threshold 

for surge and heave cues to be determined byaD. Thresholds for 

the other axes will depend on the relative magnitudes of the 

pilot-associated uR and the system-associated 8. 

The discussion to this point has concentrated on static 

"position" thresholds. To determine dynamic "velocity" thresholds 

associated with visual cueing , one might attempt to assign a value 

on the basis of pastpsychophysicalmotion detection/discrimination 

experiments. However, a review of the subject by Graham (1965) 

shows that a wide range of values can be assigned, depending on the 

particular experimental situation and empirical measures used. We 

chose to assign a value on the basis of earlier dynamic tracking 

experiments. In his manual tracking study and model analysis of 

visual threshold effects- Levison (1971) found that the data could 

be best matched in terms of OCM parameters by choosing visual 

position and rate thresholds as follows: 

Y TH = 0.071 deg 

+ TH = 0.24 deg/sec 

resulting in a ratio of YTH 

l "TH of approximately 3.4. In a 

subsequent study of tracking, with a quantized visual display 

(Levison et al (1972)), a similar model match was obtained by 

-4o- 
settingiH equal to the display quantization level, and adjusting 

Y TH to provide the best fit to the data. This resulted in 

YTH = 0.25 deg 

+TH = 1.1 deg/sec 

. 

yielding ayTH/yTH ratio of approximately 4.4. Other studies have 

shown similar ratios between velocity and position thresholds, and 

thus we chose for this study to specify the visual velocity 

threshold according to: 

-j = 4Y (3.8) 

where, if Y is given in degrees, ; is given in degrees/set. 

Hence, we tie the rate threshold to the position threshold, which, 

in turn, depends on the pilot-associated and display-associated 

resolution limitations. 

3.1.3 Informational Thresholds (Visual Channel) 

We are now in a position to define the effective 

"informational" thresholds, associated with the visual cues 

available to the pilot. As we noted earlier, we assume the pilot 

effectively "inverts" the appropriate equation of equation set 

(3.4) to obtain an estimate of the vehicular attitude/position 

change from the visual cues available to him. If we assume that 

-4l- 
the effective visual threshold Y applies equally to the azimuth 

(Q vis ) and elevation (0 vis ) LOS changes, we can then generate an 

informational threshold table as shown in table 3.2. 

The position threshold relations were obtained by inverting 

the several equations of (3.41, ignoring any sign relations (since 

the object is to identify magnitude effects), and substituting in 

the visual threshold Y or the discrimination threshold (8,/30), as 

appropriate. The velocity thresholds were obtained similarly, 

. 

except that the visual rate threshold Y was substituted 

throughout. Note that the roll threshold is divided by a factor of 

2 to account for the availability of dual endpoints and the 

potential resolution enhancement which may exist with a 

continuous-line horizon. 

Since the informational thresholds given in table 3.2 depend 

on parameters defined by the scene geometry and simulator 

configuration, we will defer presenting specific threshold values 

until we discuss the hover task in more detail in chapter 4. 

3.1.4 Visual Model Implementation 

Since the perceptual dynamics of the human visual system are 

relatively wide-band with respect to the system dynamics we are 

modelling, we chose not to include any dynamic visual effects. 

This allowed us to implement our visual perception model by simply 

-42- 
Table 3.2: VISUAL SCENE INFORMATIONAL THRESHOLD FUNCTIONS 

AXIS POSITION VELOCITY 

PITCH 

RULL(l) 

SURGE(2) 

HEAVE(*) 

(1) FACTOR OF 2 ACCOUNTS FOR TWO END-POINT HORIZON 

(2) POSITION THRESHOLD IS DISCRIMINATION LIMITED 

(3) ASSUMES VISUAL TARGET STRAIGHT AHEAD (y. = 0) 

-43- 
thresholding the appropriate system state variables: the 

linear/angular positions and velocities of the (simulated) vehicle. 

The thresholds are those given in table 3.2, and are treated 

within the OCM pilot model context as described earlier in Section 

2.2.2. 

3.2 Vestibular Perception Model ---- 

Models of vestibular motion perception have been the subject 

of study for a number of years- and it is beyond the scope of this 

report to attempt to summarize this work. Instead, we refer the 

reader to a relatively recent review of motion cue models by 

Zacharias (1978), in which a number of these models are described 

and critically reviewed. The basis for the vestibular model used 

in the current study is to be found in this review, and we 

summarize its major features here. 

Figure 3.3 shows the vestibular model in block diagram form. 

The upper portion models the semi-circular canals as transducers of 

angular velocity, while the lower portion models the otoliths as 

transducers of specific force. 

-44- 
CANAL WASHOUT 

X 

-5. 

- l-l” 

ACCELERATION 

THRESHOLD* 

fl;Is;D 

ANGULAR 

ACCELERATION 

SPECIFK FORCE Y -II 

SENSED LINEAR 

OTOLITH DYNAMICS ELERATlONrrlLT 

LACCELERATION 

THRESHOLD” 

*THRESHOLD VALUES SPECIFIED INDIVIDUALLY FOR EACH AXIS !i 

0 

Figure 3.3: Vestibular Model 

-45- 
3.2.1 Perception of Angular Velocity (canal model) 

The effective canal input is angular velocity of the pilot, 

which, for simplicity, we assume to be equal to the angular 

velocity of the vehicle. If P19r and r represent the conventional 

roll, pitch, and yaw body rates, then it can be shown that they are 

related to the conventional Euler angle rates of roll, pitch, and 

yaw ($,(!I,$) according to: 

(3.9) 

where it is assumed that the vehicle has nominal roll angle of 

zero, and a nominal pitch angle Oo. Since our hover problem 

involves a zero nominal pitch angle (eo=O), the body rates (p,q,r) 

are simply the derivatives of the Euler angles (@,e,$) as shown in 

the diagram. 

The individual body rates are processed by separate channel 

washout filters, which are used to represent the basic AC 

transduction characteristics of the canals. The filtered rate 

signals are passed through an "adaptation" washout, which is used 

to represent central adaptation to a constant motion stimulus. 

After thresholding by a velocity thresholdwo (one for each axis), 

-46- 
the filtered signals then become the basis for the pilot's 

(vestibular) sensation of angular velocity. As shown in the 

diagram, a similar parallel path exists for sensation of angular 

acceleration. 

Specific model parameter values are given in table 3.3, and 

were taken from table 5 of Zacharias (1978). Note that although 

the adaptation time constant value Ta is the same for all three 

axes, the other parameter values (TL,Wo, anda,) differ on an 

axis-by-axis basis. 

3.2.2 Perception of Linear Acceleration and Tilt (otolith model) 

The effective otolith input is specific force, defined as 

linear minus gravitational acceleration: 

f=*r’-q --A (3.10) 

For simplicity, we assume that the specific force acting on the 

otoliths to be equal to the specific force acting on the vehicle 

center-of-gravity. As before, if we assume a nominal roll angle of 

zero and a nominal pitch angleeo, then it can be shown that the 

above equation can be expressed in body-axis coordinates as 

follows: 

fx 

[I [ 

‘x’ cos 8, -i' sin e. + gec0s 8, 

. . f = 

f: 

Y - g@c0s e. 

'x' sin 0 

0 

+ 'z' cos 8, + gesin e. I 

(3.11) 

-47- 
With the nominal pitch angle 6o set to zero in our hover problem, 

this relation simplifies to that illustrated in the bottom half of 

figure 3.3. 

These individual components of specific force are processed by 

a second-order filter representing both the otolith dynamics and 

any associated central processing. The filtered signals are then 

passed through an acceleration threshold, and serve as the basis 

for the pilot's (vestibular) sensation of linear acceleration and 

tilt away from the vertical. No provision is made for an explicit 

output which signals "jerk", or rate of change of acceleration. 

Specific model parameter values are given in table 3.3. 

Filter parameter values were based on the model proposed by Young 

and Meiry (1968). The acceleration threshold was set by assuming 

an effective velocity threshold v. of 0.7 ft/sec, and computing a, 

according to: 

A detailed justification of this approach is given in the review 

cited above. Note that, in contrast to the rotational sensation 

model discussed above, the parameter values associated with 

translation/tilt sensation are the same for all three body axes. 

-4E- 
Table 3.3: PARAHETER VALUES FOR VESTiBLJLAR PERCEPTION MODEL 

Axis 

Pitch 

Roll 

Yaw 

-cL (set) -ra(sec) 

5.3 30 

6.1 30 

10.2 30 

w. (o/set) ao(l/sec2) 

3.6 0.67 

2.5 0.41 

4.2 0.41 

Otolith Parameters (all axes) 

(-+T~,T~,) = (13.2s, 5.33s, 0.66s) 

(K, a01 = (0.4, 0.053 ft/s2) 

3.2.3 Vestibular Model Implementation 

As described, the vestibular model involves a linear 

transformation, of the system state (to obtain specific force), 

-49- 
dynamic processing of these transformed state variables, and 

appropriate thresholding on the outputs. In implementing this 

model, we simply expressed the dynamics of figure 3.3 in 

state-variable form (accounting for the input linear 

transformation) and augmented the basic system state equation (2.1) 

introduced earlier in section 2.1. The velocity and acceleration 

thresholds were treated within the OCM pilot model context as 

described earlier in section 2.2.2. 

To reduce computational requirements imposed by the vestibular 

model, we performed an analysis of the power spectrum of the 

vestibular signals. By comparing the power spectra of incoming 

vestibular signals to that of their filtered outputs, pass-bands 

were identified which accounted for the majority of the correlated 

power. Utilizing this information allowed the elimination of any 

lead or lag elements having break frequencies not in the 

pass-bands. Table 3.4 outlines the resulting simplifications and 

Appendix A presents their numerical realization. Although many of 

the vestibular dynamics were simplified or eliminated, the 

vestibular thresholds given in table 3.3 were still implemented. 

-5o- 
Table 3.4 Simplifications to the Vestibular Model 

Axis Simplification 

Pitch (8) 

Roll (+I 

Yaw ($1 

Surge (fx) 

Heave (fs) 

Sway (f,) 

Eliminate canal washout and adaptation filters 

Eliminate adaptation filter 

Eliminate canal washout and adaptation filters 

No simplification 

Set -cl = ~~ = 0 

No simplification 

3.3 Attention-Sharing Model 

The general features and method of implementation of the 

attention-sharing model have already been discussed in section 

2.2.2. Here, we wish to describe features of the model which are 

specific to the particular helicopter hover task under 

consideration. 

In our modelling of the hover task, we assumed that "full 

attention" corresponds to an overall noise/signal ratio of -20 dB, 

a level which is consistent with the finding of many earlier manual 

control studies (see, for example, Kleinman et al (1970)). This 

choice sets the value of PO in our attention-sharing model 

of (2.8). 

-51-, 
. th To find the fraction of attention fi associated with the 1 

display variable, we Eirst assumed that the pilot need not share 

attention between modalities. Thus, if fVIS and fvEs are the TOT TOT 

attention levels assigned to the visual and vestibular modalities, 

we set the two levels equal to unity: 

fvis = ps = 1 (3.12) 

TOT TOT 

We made the additional assumption that, within a modality, the 

pilot would share attention equally between the (decoupled) 

longitudinal and lateral control tasks. With fVLS and fVrS 

LONG LAT 

representing the visual attention allocation between cues, we then 

have 

,VIS = fvis L = 0.5 (3.131) 

LONG LAT 

with the two summing to f VIS 

TOT' 

An identical relation holds for the 

vestibular modality: 

f VES fVES 

LONG= LAT 

= 0.5 (3.13b) 

The final breakdown of the pilot's attention-allocation policy was 

obtained by assuming that attention-sharing was required among the 

display variables associated with a given COiltrOl axis 

(longitudinal or lateral) and a given modality (visual. or 

vestibular). Thus, if f3'Is I 

xvis 

I and zvLs represent the visual 

display variables associated with longitudinal control, it was 

required that 

-52- 
fVIS + ps + fVIS = g?IS 

e X z LONG 

Similar expressions apply to the lateral channel and the vestibular 

modality.* 

Specific values for these individual display-associated 

attention levels were found by an optimization routine which set 

the values to ensure optimum hover performance (Levison et al 

(19711, Kleinman (1976)). The values themselves are presented 

later in chapter 5, after the system and task are described in more 

detail. 

As a final note on the attention-sharing model, it was assumed 

that the pilot was not required to share attention between a 

display variable and the time derivative of that variable. Thus, 

if evIs l VIS and 0 represent the visually-obtained pitch and pitch 

rate information, then the associated attention levels are 

constrained to equal one another according to: 

$m =f VIS 

.- . 

8 8 

with the understanding that f VIS is chosen to optimize performance. 

8 

(see Levison (1971) for additional discussion). 

* Altho>jh one mlghtarguethat~~rs.-ai~~~fion-sharing requirement 

is inconsistent with the "integrated" nature of the out-the-windorr 

visual display, we chose to impose this constraint to emphasize 

the "cue-sharing" which is implicit in our geometric model of 

visual cue processing (recall discussion of section 3.1.1). 

-53- 
4. TASK/SYSTEM DESCRIPTION 

This chapter defines the important helicopter and simulator 

characteristics, as well as the pilot's task objectives as they 

relate to our implementation of the optimalcontrolmodel. Section 

4.1 describes the flight task and vehicle dynamics, while section 

4.2 describes the simulator characteristics. Section 4.3 concludes 

the chapter with a description of how the simulator characteristics 

are modelled within the context of the OCM pilot/vehicle model. 

4.1 Description A----- of Flight Task --- 

The hovering flight control task has been linearized and 

decoupled into longitudinal and lateral control tasks. The 

linearized equations of motion for each control task can be 

expressed in state variable form as : 

;r = Ax + Bu + Ew - - - - (4.1) 

where x is an n - X -vector of vehicle and disturbance states, u is an 

n u-vector of control inputs, and w is an nw-vector of white noise - 

disturbance inputs. For the problems considered here the state, 

control, and noise variables are defined as follows: 

-54- 
a) Longitudinal Dynamics 

I 

i x=h T 

g~wgl’wg g 

,q ,X,:,Z,&e,61 

U - = [6e,6clT 

w= [rl u*TIw - IT 

where 

% = translational gust, longitudinal 

wg'wgl = translational gust, vertical 

qg 

= rotational gust, pitch 

x,; = longitudinal hover error, error rate 

2,; = vertical hover error, error rate 

e,B = pitch angle error, error rate 

&e = differential collective (elevator) control 

%2 

= gang collective control 

nur~w = white noise inputs to gust states 

b) Lateral Dynamics 

x = [v - g~~gl~~g~~g~~,Y,~r~,~,~l~ 

g = [6a,6rl T 

- w = DlvdlplT 

where 

-55- 
?v %1 = translational gust, lateral 

p!3 = rotational gust, roll 

% = rotational gust, yaw 

y,; = lateral hover error, error rate 

roll angle error, error rate 

GJJ = yaw angle error, error rate 

'a = roll cyclic (aileron) control 

6I = yaw cyclic (rudder) control 

rlv?rl P = white noise inputs to gust states 

Notice that the first four states comprise the gust model and 

are simply appended to the vehicle's state equations. The gust 

model used has the Dryden form with parameters appropriate to 

MILSPEC 8785B for low altitude hover.* Values for the entries of 

the A,B and E matrices corresponding to CH-47 unaugmented and -- - 

augmented control dynamics were obtained from Hoffman et al (1976), 

and are given for reference in Appendix A. A more detailed 

derivation of these equations can be found in the original 

reference. 

The hovering task is a disturbance regulation task. As is 

standard procedure for application of the OCM, it is assumed that 

the objective of the task may be characterized 

g The gust modei~-~o~-n~~i~~l~~~~~nsequences of hovering 

in ground-eEfect. 

-56- 
as minimization of the following cost functional (see Kleinman 

(1976)): 

J= E ( (yi/yi )2+ 

2 

yi"yi I (3.2) 

max 

where yi is a performance tolerance on the corresponding 

max 

variable. The values for yi were chosen to be 5 ft and 1 ft,/sec 

max 

for position (x,y,z) and velocity (x,y,z) variables and 1 deg and 

.05 deg/sec for attitude (9, e,@) and attitude rate ($,h, i) 

variables; these values were taken from Hoffman et al (1975). As 

noted earlier, the weightings on control rate activity, rj, were 

chosen by means of an error-control tradeoff analysis. This 

resulted in the following values for TN: 

a) longitudinal axis: TN = [.15,.181 (4.3a) 

5) lateral axis: TN = [.l,.ll (4.3b) 

It should be noted that hover control of the unaugmented CH-47 

is not an easy task. The results of the reference cited above 

suggest that the task cannot be performed to within acceptable 

tolerances under IFR conditions. 

4.2 Simulator Configuration --- 

In this section, we provide brief functional descriptions of 

the three major components of the simulator facility: the 

main-frame computer, the CGI, and the V?%3. 

-57- 
4.2.1 Main-Frame Computer 

For our study we assumed the vehicle equations of motion were 

implemented on a digital computer, operating at a nominal update 

rate of 30 Hz. Based on results from the analytic study by Baron 

et al (1978), we assumed for simplicity that the integration 

routine introduced no "distortion" in the continuous vehicle 

dynamics being modelled, and that the only effect of digitization 

was the introduction of a sample and hold delay associated with the 

base cycle time of the main-frame computer. 

4.2.2 CGI Characteristics 

Nominal CGI characteristics were chosen from the range of 

specifications provided in the original statement-of-work. Table 

4.1 summarizes the parameter set used to define nominal CGI 

configuration used in the analytic studies described later in 

chapter 5. The nominal field-of-view specification is ilLustrated 

in figure 4.1 as screen configuration B. 

Off-nominal CGI characteristics were also considered. These 

included variations in the refresh rate, the effective sample rate, 

the field-of-view (illustrated as alternate screen configurations A 

and C in figure 4.1), and the display resolution. The impact of 

these variations on performance will be discussed later in 

chapter 5. 

- 58- 
Table 4.1: Nominal CGI Characteristics 

Picture Refresh Rate 30 frames/s 

Display Compute Time 66 msec 

Effective Sample Rate/Delay 15 Hz/99 msec 

Scene Content 6000 edges/frame 

Field-of-View 3 screens across (144O horiz,36Overt) 

Display Resolution 1024 lines/frame x 1024 pixels/line 

No CGI Lag Compensation 

4.2.2 VMS Characteristics 

Nominal VMS characteristics were set according to the 

specifications provided in the original statement-of-work. Figure 

4.2 is a block diagram of a single-axis of the six 

degree-of-freedom system, incorporating both a second-order dynamic 

model and appropriate position/rate/acceleration servo limits. 

Table 4.2 defines the nominal parameter values associated with 

each motion axis. 

4.3 Model Implementation of Simulator Characteristics 

To use the OCM for predicting pilot performance in the 

simulator environment, it is first necessary to express the above 

simulator characteristics in terms of relevant OCM parameters. 

This section provides a brief description of the method for mapping 

-59- 
CONFIG SIDE VIEW 

@ 

m 

x 

(NOMINAL) 24O 

TOP VIEW 

x 

x 

Figure 4.1: CGI Screen Configuration 

-6O- 
Figure 4.2: WlS MODEL 

TRANSDUCER 

‘L POSITION 

TRANSDUCER 

LINEARIZED TRANSFER FUNCTION: $ = 

t;; 

c S2+ 2pqs+w,L 

Table 4.2: VMS Model Parameters 

AXIS 

-_-._. -~ . . ._ ^. 

ROLL ($1 

PITCH (9) 

YAW WI 

SURGE (x) 

WAY (Y) 

HEAVE (z) 

* RAD/S 

WY n X” 

9.4 007 w/s2 

914 007 3.7 2 159s -220 22O 

5=r"/sws2 159s -24" 26O 

150/s -29O 2g" 

9,4 0,7 16FT/S2 2FT/S -?,5FT Z,~FT 

18.5 il.7 24v/s2 

~~FT/s~ 

~OFT/S -2oFT 29~~ 

13.8 0,7 2gFT/S -3T)FT 3oFT 

-61- 
from simulator to OCM parameters, and summarizes the specific OCM, 

parameter values associated with the nominal simulator 

configuration. 

4.3.1 Main-Frame Computer and CGI Parameters 

Figure 4.3 illustrates the general mapping from the computerand CGI-associated parameters set to the OCM-associated parameter 

set. Solid lines are used to represent the three basic mappings 

used in this study.* As shown, the OCM parameter associated with 

visual path delay is impacted both by the main-frame computer rate 

and by the CGI-associated parameters specifying the display delay, 

refresh rate, and update rate. The OCM parameter associated with 

motion path delay depends solely on the main-frame computer delay. 

Finally, the OCM parameters associated with the visual scene 

thresholds are impacted by three CGI parameter sets: those 

specifying the scene geometry, the CGI resolution, and the screen 

field-of-view. We discuss these mappings in more detail in the 

following paragraphs. 

* Dashed lines represent more speculative mappings which we?e 

considered but not implemented during the course of the study. 

-62- 
CGI 

PARAMETERS 

OCM 

PARAMETERS 

COMPUTER 

PARAMETERS 

DISPLAY DELAY 

VISUAL DELAY 

UPDATE RATE 

UPDATERATE 

\ 

RESOLUTION 

Figure 4.3: Simulator Mappings for OCM Analysis 

-63- 
Visual and Motion Delays --.-.--- - 

Figure 4.4 illustrates how the main computer and CGI 

characteristics determine both the visual and motion path delays. 

The main computer is modelled as an ideal sampler acting on the 

pilot's continuous control signal, followed by a dead-time, a 

synchronous sampler, and a zero-order hold. The dead-time is 

assumed to be equal to the sample period TC, which, in turn is 

assumed to be an integral multiple of a base period To. 

The output of the hold drives the VMS. Thus, the delay 

between the pilot's stick input and the output to the VMS is 

obtained by summing the dead-time delay TC with the effective 

dead-time generated by the hold, to yield the motion path delay: 

1 

'mot = T, + zfc = (3/2)TC (4.4) 

The output of the main-frame hold also drives the CGI, which, 

as shown in the figure, is also modelled as a dead-time, two 

synchronous samplers, and a zero-order hold. The samplers and 

output hold circuit model the refresh characteristics of the CGI 

(TR is the refresh period), while the dead-time models the 

computational time TD required to generate the visual scene from 

the signal generated by the main-frame computer. Both can be 

independently specified, although, as shown in the figure, we 

assume both to be integral multiples of a base period To. 

-64- 
(TC = mTo) 

MAIN COMPUTER 

SAMPLE RATE: fC= +- 

- to VMS 

(TR =kTo ,T,, =nT, ,T, >,T,) 

L DISPLAY COMPUTER & DISPLAY 

1 

SAMPLE RATE: f, = K 

, 

OVERALL SAMPLE RATE: f,fi =I MIN (f,, to) 

VISUAL DELAY: rvis = Tc + T + L 

D 2feff 

. 

MOTION DELAY: Tmot = T, + $- = 9 T, 

C 

Y vis 

Figure 4.4: Visual and Motion Path Delays 

-65- 
The sample rate fC of the main computer is simply l/TC. We 

assume, for all CGI configurations considered, that the refresh 

period TR will be less than or equal to the computational time TD. 

Thus, the effective sample rate fD through the CGI is l/TD. With a 

main-frame computer rate of fc, this implies that the overall 

effective sample rate associated with the visual path is given by 

f eff = MIN(fC,fD) (4.5) 

This, in turn, determines the overall visual path delay,Td, since 

it is obtained by simply summing the main-frame delay TC, the CGI 

dead-time TD, and the effective dead-time generated by the CGI 

hold, l/(2feff), to yield: 

T VIS = TC + TD + (l/2feff) (4.6) 

This expression takes advantage of the fact that the delay 

contributed by the main-frame hold is negated by the CGI's input 

sampler. 

The nominal simulator configuration has a CGI refresh rate of 

30 Hz (TH = 33 msec) and a CGI computation time of 66 msec, so that 

the effective CGI display rate fc is 15 Hz. With a nominal 

main-frame rate of 30 Hz, (4.5) implies an effective visual path 

sample rate of 15 Hz, leading to a visual path delay of 132 msec, 

based on (4.6). The motion path delay is (3/2)Tc, or 50 msec 

-66- 

_ _ 
(based on (4.4)). A similar computation of the visual and motion 

delays is equally straightforward using (4.4) through (4.6), for 

other choices of simulator parameter values. 

In modelling these delay paths, we chose to increment the 

pilot's delay-r(which delays all display variables) by the smaller 

of the two delays, in this case, the motion delay of 50 msec.* The 

visual delay was then implemented by passing each visual display --_- 

variable through a first-order Pade filter, with the effective 

delay of the filter set equal to the difference between the visual 

and motion delays. In this way, the filter delay sums with the 

incremental pilot delay, to yield the desired effective visual 

delay. 

Visual Thresholds -I--_---.- 

In section 3.1 we discussed how visual resolution and 

discrimination thresholds led to informational thresholds 

associated with visually-obtained display variables. Table 3.2 of 

that section demonstrated how these thresholds are dependent on 

both the effective resolution threshold and the scene geometry. In 

this section we determine nominal values for these visual 

parameters, and specify the informational threshold levels. 

e-e -- * Due to an earlyxange in the nominal configuration, the motion 

delay was inadvertently set to 33 msec, rather than the desired 50 

msec. This 17 msec difference makes little difference in overall 

hover performance, however , as the results of chapter 5 will show. 

-67- 
The angular resolution threshold imposed by the CGI is 

dependent on the screen frame size, the number of lines per frame, 

and the number of pixels per line. Taking the frame size as given 

in table 4.1, and assuming, for simplicity, that the line/pixel 

density is N lines/frame and N pixels/line, then the horizontal and 

vertical resolution limits will be: 

6 

a = 48O/N, Bv = 36O/N (4.7) 

An approximate (directionless) CGI resolution threshold can then be 

obtained by averaging the horizontal and vertical limits: 

6 = PH + ++ /2 (4.8) 

For the nominal configuration of 1024 lines/frame and 1024 

pixels/line this yields a CGI-imposed angular resolution limit of 

about 0.042 deg, smaller than the human operator threshold of 0.05 

deg specified by (3.5a). In accordance with (3.7), the effective 

resolution threshold is thus set by the pilot's limitations: 

y = 0.05 deg (4.9a) 

This, in conjunction with (3.8), defines the angular rate threshold 

for the nominal CGI configuration: 

; = 0.20 deg/sec (4.9b) 

-6% 
One of the geometric factors determining the visual scene 

informational thresholds is the lateral field-of-view, QFOV, 

Reference to table 3.2 of section 3.1.3 shows that the roll 

threshold +TH is inversely proportional to QFoV, so that the 

minimum threshold is obtained with the maximum field-of-view. In 

the simulator environment, this is determined by the CGI screen 

configuration, and, as can be seen from figure 4.1, the nominal 

configuration has a 72O half field, so that: 

4) FOV = 72O (4.10) 

For screen configurations A and B, this figure would be 24O and 

72O, respectively. 

A second geometric factor impacting the thresholds is the 

line-of-sight (LOS) depression angle 8, for surge (x) axis 

information. Reference to table 3.2 shows that 

XTH 0 

(4.11a) 

'TH Ql/sin20 

0 

(4.11b) 

so that xTH and kTH are minimized wheneo is selected to be 67O and 

9o", respectively. Since figure 4.1 shows that the allowable 

maximum 8 is 24O 

0 

for the nominal screen configuration, we can 

minimize the surge thresholds by choosing e. according to: 

-69- 
Oo=24" (x-axis) (4.12) 

For screen configurations A and C, this figure would be 24O and 

60°, respectively. 

The LOS depression angle O. for heave (z) axis information 

also impacts the threshold values. From table 3.2, we have 

~~~~e~o/sin~~cose~ (4.13a) 

'TH ~l/sin~ocOs~o (4.13b) 

. 

so that zTH and zTH are minimized wheneo is selected to be O" and 

4s", respectively. Since these thresholds are only moderately 

sensitive toeo, we chose to select a single compromise value for 

both position and rate display variables: 

8 = 22s" 

0 (z-axis) (4.14) 

Reference to the screen geometry of figure 4.1 shows this LOS angle 

to be unconstrained by the CGI configuration, and thus applicable 

to all three configurations. 

With the hover attitude ho and visual reference range 1, 

specified earlier by (3.6), the resolution thresholds specified by 

(4.9), and the geometric LOS factors specified by (4.10), (4.12) 

and (4.14), it is then a direct matter to calculate the visual 

scene informational threshold levels for the nominal configuration. 

-7o- 
This is done in table 4.3, which shows values for not only the 

nominal (configuration B with 1024 lines/frame), but also for 

off-nominal screen geometries with finer and coarser resolution 

levels. 

This then completes our discussion of the main-frame computer 

and CGI parameter mappings into specific OCM-associated parameter 

values. We now turn briefly to a discussion of VMS-associated 

parameter values. 

4.3.2 VMS Parameters 

As illustrated earlier in figure 4.2, the VMS model 

incorporates both second-order dynamics and appropriate 

position/rate/acceleration servo limits. Since no effective motion 

thresholds were assumed to be introduced by the VMS (e.g., those 

due to stiction or hysteresis), there is no necessity for the type 

of informational threshold mapping discussed in the previous 

section. The dynamics, however, must be accounted for, and this 

was done simply by specifying an appropriate set of state equations 

for each VMS axis, and augmenting the system state equation (2.1) 

to account for the low-pass filtering introduced by the VMS. With 

a second-order filter associated with each of the six motion axes, 

this resulted in an increase of the overall system order by 12 

states. 

-71- 
Table 4.3: VISUAL SCEtiE INFO,RMATIONAL THRESHOLD VALUES 

VISUAL SCEilE IiIFORMATIO!iAL THRESHOLD VALUES 

THRESHOLD' 

ROTATION 

-RANSLAT IOI 

RESOLUTION, 500 LINES/FRAME 

I 

CONFIG. A B C 

,a 808 ,08 ,05 .05 ,05 

,34 ,34 ,34 ,20 ,20 ,20 

.08 ,08 ,08 ,35 ,05 .05 

,34 ,34 ,34 820 .20 .29 

I 10 ,04 ,011 ,06 ,02 .02 

,112 816 a16 .25 ,09 .oa 

ICI "4,84 ,117 

836 a36 .08 

137 ,37 ,37 

117 ,17 ,17 

,07 ,07 -07 

,30 ,30 ,30 

,84 ,84 ,47 

,21 ,21 ,05 

37 ,37 ,37 

10 .I0 .lO 

,04 ,04 ,[34 

,17 ,17 .17 

1000/2000 LINES/FRAME 

l * 

A B c 

l ROTATION THRESHOLD VALUES IN DEG s, DEG/SEC 

TRANSLATION THRESHOLD VALUES IN FT & FT/.SEC 

l * NOMINAL CONFIGURATION 

-72- 
5. CLOSED-LOOP ANALYSIS OF CGI AND VMS EFFECTS 

In this chapter, the optimal control model with the expanded 

perceptual model is used to analyze the effects of CGI and VMS 

limitations on closed-loop performance. The flight task considered 

is low altitude hover in turbulence of a CH-47 helicopter. 

Performance decrements from ideal or perfect simulations (i.e., 

flight) are computed for nominal CGI and VMS configurations. The 

effects of individual simulation parameters are also considered. 

5.1 Simulator Configurations 

The goal of this analysis is to determine the effects of CGI 

and VMS characteristics on simulator fidelity (more precisely, 

performance and workload). To this end, a "perfect" or ideal 

simulator is defined in which there are no simulation time delays, 

no motion system dynamics, and an infinite resolution imagery 

system. This simulator configuration corresponds essentially to 

flight* and provides a benchmark against which to measure simulator 

deficiencies. 

* Through-‘<n6-6versight, thr----- ' ---- -- assumptions for the perfect 

configuration included a field-of-view constraint relevant to the 

nominal CGI configuration. This degraded performance only 

slightly from what would have been obtained without the 

constraint. 

-73- 
Realistic or nominal CGI and VMS configurations were defined 

in section 4.3. For convenient reference, we have summarized the 

pertinent nominal simulator characteristics in table 5.1. Included 

in the realistic configuration is a main-frame digital computer 

delay of 33 msec, corresponding to a 30 Hz computation rate. 

The nominal motion system did not include washout filters as 

all predicted motions except the surge (x) motion were well within 

their respective simulator limits. A surge motion washout filter 

was designed for the task and its effects evaluated; this will be 

discussed later. In addition to the nominal and perfect motion 

conditions, results were also obtained for a "no-motion" or 

fixed-base simulator configuration. 

Thus, there were six basic simulator configurations to be 

analyzed so as to evaluate the effects of the visual and motion 

systems, separately and together. These configurations are listed 

in Table 5.2. 

5.2 Pilot Model Parameters ----. -- 

The parameters of the optimal control model pertaining to 

informQtion processing limitations were set on the basis of 

previous studies and the perceptual analyses described in Chapter 

3. The numerical values and basic assumptions are summarized in 

Table 5.3. 

-74- 
Table 5.1 REALISTIC (NOMINAL) SIMULATOR CONFIGURATION 

CGI CHARACTERISTICS 

Picture refresh rate 30 Frames/s 

Display compute time 66 msec 

Effective Sample rate/delay 15 Hz/99 msec 

Scene content 6000 edges/frame 

Field-of-view 3 screens across (144O horiz, 

36O vert) 

Display resolution 1024 lines/frame x 1024 

pixels/line 

VMS CHARACTERISTICS* 

2nd order dynamics (all axes) 

Position, rate, & acceleration limits (all axes) 

No washout filters 

*see Table 4.2 for specific parameter values 

-75- 
Table5.2 SIMULATOR CONFIGURATIONS 

- 

CONFIGURATIOi4 DESCRIPTION 

.~ 

I Perfect (Flight) No simulator delays, nominal field of view, 

human operator thresholds, no VMS dynamics 

Perfect CGIRealistic VMS 

Realistic CGIPerfect VMS 

Includes main frame computer delays and 

VMS platform dynamics 

Includes main frame and display computer 

delays, CGI imposed visual thresholds, no 

platform dynamics 

Realistic CGIRealistic VMS 

Perfect CGIFixed Base 

Includes all simulator nominal 

characteristics (see Table 5.1) 

Includes main frame computer delays in 

visual cues, no motion cues 

I Realistic CGI- I Includes CGI limi+8tion~; FC ~.sticn CGCS ! 

i Fixed Base i I 

-7G- 
TABLE 52: NOMINAL PILOT MODEL 

o TASK OBJECTIVE 

MAINTAIN FOLLOWING RMS HOVER ERRORS: 

ATT I TUDE 1 DEG 

ATTITUDE RATE 0, 

POSITION 

VE LOC I TY 

o I;JFORMATIOd-PROCESSIiIGKONTROL-BANDWIDTH LIMITATIOX 

OBSERVATION NOISE/SIGNAL RATIO 

INTERNAL TIME DELAY 2” tB 

MOTOR TIME CONSTANT 8 al F s 

o VISUAL PERCEPTIO?! MODEL 

PERSPECTIVE/GEOMETRIC CUES 

NO SENSORY DYNAMICS 

RESOLUTION/DISCRIMINATION THRESHOLDS 

o MOT 1014 PE RCEPTI 0;“ MODEL 

ROTATIONAL AND SPECIFIC FORCE CUES 

VESTIBULAR DYNAMICS (CANALS & OTOLITHS) 

RESOLUTION THRESHOLDS 

o ATTENTION-SHARING MODEL 

SHARED ATTENTION BETWEEN LONGITUDINAL AND LATERAL AXES 

NO INTERFERENCE BETWEEN MODALITIES 

OPTIMUM SHARING WITHIN MODALITIES 

-77- 
5.3 Results and Discussion --de.---- -I 

The effects of CGI and motion system characteristics will be 

examined largely in terms of relative performance in the hovering 

task. For each axis, relative performance is defined as 

Performance (in %) = 100 x (J-JFLT)'JFLT (5.1) 

where J is the value of the cost functional of (4.2) and JFLT 

corresponds to the value of J obtained for flight or the "perfect" 

simulator. Thus, relative performance is a normalized metric of 

performance that measures the percent deviation from "flight" 

performance introduced by the simulator characteristics. In this 

sense, relative performance is a measure of simulator fidelity. 

The results will be presented in terms of J (rather than 

individual error and control scores) because t!lis quantity is a 

scalar metric of overall performance and, therefore, provides a 

concise description of the simulator effects. In addition, Hess 

(1977) has shown that the value of J may be correlated with 

vehicle flying qualities, so increases in J owing to simulator 

deficiencies may be related to degraded flying qualities for the 

simulator. Nonetheless, individual error and control scores may 

also be of interest and these are presented later in table 5.5 for 

the six simulator configurations. 

-78- 
Table 5.4 summarizes the optimal attention split found for the 

nominal fixed and moving base simulator configurations. As 

discussed in Kleinman (19761, the attention paid to a variable 

indicates its relative importance to the task. Table 5.4 shows 

that pitch and pitch rate information is the single most important 

visually obtained variable for the longitudinal axis, while roll 

and roll rate are most useful for lateral control. Of the 

vesti'oular cues, pitch, pitch rate, and z-axis specific force are 

all important for longitudinal control , while y-axis specific force 

is the most useful cue for lateral control. 

5.3.1 Overall CGI and Motion System Effects 

Figure 5.1 presents the model performance predictions for the 

five simulator configurations, relative to that expected from the 

"perfect" simulator (which, by definition, has a relative 

performance of zero). With respect to longitudinal performance 

(figure 5.la), it can be seen that the effect of the CGI is much 

more significant ( 35%) than that of the motion system ( 10%). 

Indeed, performance is better with a perfect CGI and no motion than 

with perfect motion and a realistic CGI. However, motion is still 

important, particularly if the realistic CGI deficiencies are 

accounted for. This is shown by the prediction of approximately 

twice the relative performance for the realistic CGI-fixed base 

configuration as for the realistic CGI-realistic VMS configuration. 

-79- 
Table 5.4: ATTENTION ALLOCATION FOR NOMINAL CONFIGURATIONS 

MOVING FIX9 

VARIABLE BASE BASE 

X,i 

z,; 

e,8 

FX 

F” L 

e, P 

0.047 0.045 

0.087 0.053 

0,366 0,402 

0,500 0,5m 

0,021 

0,137 

0,291 

J 0,5r)o 

I 

z 0,195 0,085 

m 0,366 0,336 

;: 

v) Lt.,+ 0,029 0.026 

a 0,500 0,500 

- 

742 

-co- 
Gitr 

-7 IA 

I 7 

‘) 

- 

80 

80 

40 

20 

0 

- 

- 

- 

REALISTIC VMS PERFECT VMS REALISTIC VMS PERFECT CGI REALISTIC CGI 

PERFECT CGI REALISTIC CGI REALISTIC CGI FIXED BASE FIXED BASE 

Figure 5.la: Relative Performance Vs. Simulator Configuration 

(CH-47 Longitudinal Axis) 
REALISTIC VMS PERFECT VMS REALISTIC VMS PERFECT CGI REALISTIC CGI 

PERFECT CGI REALISTIC CGI REALISTIC CGI FIXED BASE FIXED BASE 

Figure 5.1.b: Relative Performance Vs. Simulator Configuration 

(CH-47 Lateral Axis) 
The results for the lateral control task (figure 5.lb) are 

similar to those for the longitudinal task, but motion is even more 

important. In this case, having a perfect CGI does not compensate 

for lack of motion, since t'ne fixed base configurations are worse 

than any other motion configuration. Compared to the longitudinal 

task, going from perfect to realistic motion introduces less 

performance degradation. Also, motion ameliorates the consequences 

of any visual deficiencies. 

For either longitudinal or lateral control, the performance 

change (lo-15%) due to introducing the realistic motion system 

alone is probably within the inter- and intra-pilot variations that 

might be expected. However, once realistic CGI effects are 

considered, or motion is removed entirely, this is no longer likely 

to be true for skilled pilots inasmuch as the deviations predicted 

can be substantially greater than 20%. 

Table 5.5 gives the effects of simulator configurations on 

individual rms error scores. These scores generally tend to 

parallel the effects shown for relative performance, as would be 

expected. However, it is interesting to note that predicted 

control scores are less affected by simulator changes than are 

output variables. Indeed, the control scores remain fairly 

-33- 
Variable Units 

ft 

ft/sec 

ft 

ft/sec 

deg 

deg/sec 

ft 

ft/sec 

deg 

deg/sec 

d-3 

deg/sec 

inches 

inches 

inches 

inches 

*P Perfect 

R Realistic 

FB Fixed Base 

Augmented CR-47 

Unaugmented CR-47 Dynamics Dynamics 

Simulator Configuration 

H VMS P VMS R VMS F B P Sim P CGI R CGI R CGI P.CGI EGI 

4.05 5.14 5.72 5.93 5.49 

0.92 0.80 1.09 1.13 1.04 

1.77 1.91 2.06 2.11 2.11 

0.60 0.66 0.67 0.70 0.78 

0.82 0.87 0.96 1.00 0.92 

0.85 0.92 0.99 1.03 0.97 

6.50 6.91 7.46 7.52 8.37 

1.27 1.35 1.44 1.46 1.65. 

1.72 1.76 1.81 1.82 1.90 

1.05 1.1 1.18 1.20 1.27 

0.42 0.44 0.37 0.47 0.46 

0.15 0.16 0.15 0.17 0.20 

0.23 0.23 0.23 0.24 0.23 

0.14 0.15 0.15 0.15 0.15 

0.16 0.16 0.17 0.17 0.17 

0.03 0.03 0.03 0.03 0 .O3 

6.87 

1.30 

2.47 

0.87 

1.13 

1.17 

9.72 

1.91 

2.06 

1.46 

0.62 

0.24 

0.24 

0.16 

0.19 

0.04 

P Sim R VMS F B * R Ci-I R CGI 

2.76 3.05 3.32 

0.55 0.60 6.50 

0.99 1.12 1.31 

0.39 0.42 0.56 

0.5 0.54 0.56 

0.43 0.46 0.45 

4.31 4.56 5.63 

0.88 0.93 1.15 

1.53 1.54 1.62 

0.65 0.67 0.73 

0.06 0.06 0.07 

0.04 0.05 0.05 

0.24 0.24 0.24 

0.12 0.12 0.13 

0.27 0.27 0.28 

0.04 0.04 0.04 

Table 5.5 RMS PERFORMANCE SCORES 
constant over the various conditions.* 

The above model predictions are based on the assumption that 

the pilot will maintain a fixed level of attention for the 

longitudinal and lateral control tasks regardless of simulator 

configuration. However, in actuality, the pilot may choose to 

devote more (or less) attention to the control tasks, based on 

simulator configuration. To explore the effects of such a change 

in strategy, model predictions were obtained for various attention 

levels. The results are presented in figure 5.2. It can be seen 

that the relative ordering of simulator configurations is 

maintained at all levels of attention. At high levels of 

attention, the performance with the realistic CGI-perfect VMS 

configuration approaches that for the realistic WE-perfect CGI 

configuration. Apparently, if the noise/signal ratio is lowered 

sufficiently on the motion cues, it can offset some of the visual 

deficiencies associated with the nominal CGI. 

If it is assumed that the pilot adapts his behavior and 

increases attention levels to achieve performance equivalent to 

that in flight, then the incremental attention required may be 

considered a workload penalty associated with the simulator. The 

curves of figure 5.2 can be used to determine this workload penalty 

*-a result, inner loop variables (Band Q) are somewhat less 

affected than outer loop quantities (x,y,z and JI). 

-35- 
I I I I 

I I ,-REALISTIC (NOMINAL) CGI - PERFECT VM 

REALISTIC (NOMINAL) CGI & VMS 

REALISTIC (NOMINAL) VMS - PERFECT C( 

y\\ r FIXED BASE - REALISTIC Cc 

-11 -14 -17 

NOISE/SIGNAL RATIO (dB) 

-20 -23 

% 

T! 

1 I I I I I ;r 

0.125 0.25 0.5 1.0 2.0 

ATTENTION TO TASK 

Figure 5.2a: Effect of Workload (CH-47 Longitudinal Axis) 

-86- 
REALISTIC (NOMINAL) VMS - PERFECT CGI 

PERFECT VMS-REALISTIC (NOMINAL) CGI 

REALISTIC (NOMINAL) VMS 81 CGI 

FIXED BASE - REALISTIC (NOMINAL) CG 

-11 -14 -17 

NOISE/SIGNAL RATIO (dB) 

-20 -23 

0.125 0.25 0.5 1.0 2.0 

ATTENTION TO TASK 

Figure 5.2b: Effect of Workload (CH-47 Lateral Axis) 

-37- 
I 

for maintaining flight level performance in the simulator; one 

simply determines the intersection of the particular sensitivity 

curve with the line of zero relative performance. The computed 

attention or workload penalties for the various configurations 

analyzed in Figure 5.2 are given in table 5.6. For the nominal CGI 

and motion system, the pilot would have to increase attention by 

50% over that needed in flight in order to achieve the same 

performance, whereas almost three times as much attention is 

required for a fixed base simulation. 

Table 5.6 SIMULATOR WORKLOAD PENALTIES 

I - Attention - 

Condition I I 

Long. Lat. Total 

R. VMS, F?. CGI 

p.. VMS, R. CGI 

R. VMS, R. CGI 

R. CGI - F. B. 

- 

. 5 

.55 

.66 

. 5 

-53 

.69 

. 76 . 76 

1.25 1.5 

1.00 

1.13 

1.35 

1.52 

2.75 

-3s 
5.3.2 Effects of CGI Parameters 

The results of the previous section suggest that the visual 

processing limitations introduced by a nominal CGI configuration 

could result in significant deteriorations of closed-loop hover 

performance. Here, we examine the effects of variations in 

individual, design-related CGI parameters. In these analyses, a 

single parameter is varied while all other CGI parameters are kept 

at their nominal or realistic values. Results will be presented 

for both realistic motion base and fixed base configurations. 

Figures 5.3 and 5.4 show the effect of incremental delays on 

relative performance for motion-base and fixed-base simulators, 

respectively. Results are presented as a function of CGI display 

computer delay, for three values of main-frame computer delay (Tc). 

RecalS, the nominal display delay is 99 msec (".l set). For the 

range of'delays considered, relative performance appears to degrade 

linearly as a function of either display delay or main-frame delay, 

when motion is present. Comparison of figures 5.3 and 5.4 (note 

the difference in scale) reveals that the absence of motion cues 

will accentuate the deterioration of performance for a given delay. 

Moreover, for a fixed base configuration, performance degrades more 

rapidly than linearly. It can also be seen from these figures that 

the longitudinal control task is more sensitive to increases in 

delay than is the lateral task, particularly to increases in 

display delays. 

-89- 
Tc = 0.066 

T, = 0.033 

Tc = 0.0 

60 

0 

0 0.05 0.1 0.15 0.2 0.25 

DISPLAY COMPUTER DELAY (sec) 

Figure 5.3a: Relative Performance vs. Time Delay 

(Longitudinal Axis - Moving Base) 

80 I I I I 

T, = 0.066 

0 

0 0.05 0.1 0.15 0.2 0.25 

DISPLAY COMPUTER DELAY (set) 

Figure 5.3b: Relative Performance vs. Time Delay 

(Lateral Axis - Moving Base) 

-9o- 
T, = 0.0 

I I I I I 

0 0.05 0.1 0.15 0.2 0.25 

DISPLAY COMPUTER DELAY (WC) 

z 60 

2 iii 

K 

40 

I 

,’ LL 20 

/ 

/ 

/ 

L 

0 

I I I I I 

0.05 0.1 0.15 0.2 0.25 

DISPLAY COMPUTER DELAY (WC) 

Figure 5.4a: Relative Performance Vs. 

Time Delay (Longitudinal 

Figure 5.4b: Relative Performance Vs. 

Axis - Fixed Base) Time Delay (Lateral Axis - 

Fixed Base) 
In general, the magnitude of the effects of display delay are 

quite significant. Increasing display delay from zero to the 

nominal, but reasonably conservative, value of 99 msec, causes an 

increase in relative performance of approximately 20-30% for the 

motion-base simulation and about 40-50% for the fixed-base case. 

An examination of the relative performance values for zero display 

and computer delay shows that the effects of other CGI or motion 

system limitations are much less significant (at nominal values) 

than are the effects due to delays. 

The effects of field-of-view and display resolution are 

presented in figure 5.5. Recall that screen configuration B is the 

nominal configuration corresponding to a 144O horizontal, 36O 

vertical field of view. Configurations A and C provide 48O by 36O 

and 144O by 72O fields of view, respectively. The nominal display 

resolution is 1024 lines. Both field of view and display 

resolution are assumed to affect observational thresholds as 

discussed in Section 3.3 and summarized in Table 4.3. 

It can be seen from figure 5.5 that decreasing the horizontal 

field of view (configuration A) does not affect longitudinal 

performance and increasing the vertical field-of-view has no effect 

on lateral performance. This is expected because of the assumed 

-92- 
I 

\D 

W 

I 

140 

120 

7 0 

z 

20 

0 

SCREEN 

CONFIGURATION 

-A B C A 0 C A B C A B C 

MOVING BASE FIXED BASE MOVING BASE FIXED BASE 

----.--1--. -- e. .---- 

DISPLAY 

RESOLUTION - 

500 LINE 1000 LINES 

Figure 5.5a: Relative Performance Vs. Field of View and Display Resolution 

(CH-47 Longitudinal Axis) 
I 

w 

,b 

I 

140 

120 

20 

II 

SCREEN -A B C A B C A B C A B C 

CONFIGURATION MOVING BASE FIXED BASE MOVING BASE FIXED BASE 

--- M.W-- 

DISPLAY 

RESOLUTION - 

500 LINE 1000 LINES 

Figure 5.5b: Relative Performance Vs. Field of View and Display Resolution 

(CH-47 Lateral Axis) 
decoupling between longitudinal and lateral control tasks.* Figure 

5.5 also suggests that increasing vertical field-of-view has very 

little performance payoff and probably would not be justified on 

the basis of these results. On the other hand, the improvement in 

performance with increased lateral field-of-view appears to be 

significant, especially if the cue presentation is degraded in 

other ways, such as poorer resolution or no motion. For the 500 

line display, fixed base configuration , reduction of the horizontal 

field-of-view from 144O to 48O degrades relative performance by 

more than 30%. 

The effects of display resolution are somewhat different than 

for field-of-view in that a greater effect is observed for the 

longitudinal task than the lateral task. With motion, longitudinal 

performance is about 20% poorer for the 500 line display as 

compared to about a 5% degradation in the lateral case; for the 

fixed-base configurations, these effects are increased to about 

25-30% and lo%, respectively. 

In order to determine whether the results obtained were highly 

dependent on the particular values assumed for thresholds as a 

function of CGI configuration, a sensitivity analysis was 

* The possible effects of increased field of view providing useful 

peripheral information on vehicle rates have not been examined 

here. 

-95- 
performed. In this analysis, thresholds associated with state 

variables (and their corresponding rates) were varied, individually 

or covaried based on the display threshold modelling described in 

section 3.1. At the same time other thresholds remained fixed, so 

that relative sensitivity to variations in each could be 

determined. The results are plotted in figure 5.6. Points 

corresponding to the nominal threshold values are displayed on the 

curves. These results show that errors in the assumptions about 

thresholds on positional variables (or their derivatives) would not 

change the overall predictions very much, with the possible 

exception of the threshold on lateral deviation, y. The 

performance predictions are more sensitive to variations in 

attitude thresholds, particularly the pitch threshold. For pitch, 

halving the threshold improves relative performance by about 25%, 

whereas doubling the threshold degrades performance by about 75%. 

In general, it seems likely that the predictions of the 

effects of threshold are reasonably robust with respect to 

variations in the values used. Moreover, if thresholds on pitch 

are not estimated correctly, the results will be changed much more 

if the nominal value is less than the true value than if the 

converse is true. Given that either type of error in estimating 

the pitch threshold is equally probable, the predictions 

corresponding to the nominal value are conservative; i.e., the 

relative performance is more likely to be worse than predicted than 

-9G- 
200 c I I I I 

FIXED BASE 

MOVING BASE 

01 I I I I 1 

0.0 0.2 0.4 0.6 0.8 1.0 

X THRESHOLD -(ft) 

Figure 5.6a: Relative Performance Vs. X Threshold 

Lu 200 I I I I 1 

FIXED BASE 

MOVING BASE 

w’ 

a 

$ L3 

0 I ~~_ I I I ii 

0.0 0.1 0.2 0.3 0.4 0.5 

ii: THRESHOLD - (ft/sed 

. 

Figure 5.6b: Relative Performance Vs. X Threshold 

-97- 
MOVING BASE 

0.0 0.2 0.4 0.6 0.8 1.0 

Z THRESHOLD -(ft) 

Figure 5.6~: Relative Performance Vs. Z Threshold 

z 200 I I I I 1 

z FIXED BASE 

MOVING BASE 

i K 0. I 

0.0 0.1 0.2 0.3 0.4 0.5 

i THRESHOLD - (ft/secl 

. 

Figure 5.6d: Relative Performance Vs. Z Threshold 

-9b- 
IA 200 

Y 0 

. 

l- FIXED BASE 

MOVING BASE 

z 

m 

5 

y’ a 

0.0 0.03 0.06 0.09 0.12 

8 THRESHOLD-(degrees) 

I 

0.0 

I I I I 1 

0.12 0.24 0.36 0.43 

e’ THRESHOLD -(degrees/set) 

Figure 5.6e: Relative Performance Vs. 0 And 6 Thresholds 

200 ~ 

--..~-. 

r I I I 

MOVING BASE 

uJ7 

> -L 

F 

u 

K 0 -..- 1. I I I 

0.0 0.03 0.12 

L 

0.0 

I I I I I 

0.12. 

4 THg:HOLD (d&~s/sec) 

0.48 

Figure 5.6f: Relative Performance Vs. $I And $ Thresholds 

-99- 
MOVING BASE 

I I 

0.4 0.6 

y THRESHOLD (ft) 

,’ 5 

- 1. ..-I a 

0.8 1.0 

I 

0.0 

I 

0.8 

I I 

1.6 2.4 

i THRESHOLD (fthec) 

I I 

3.2 4.0 

. 

Figure 5.6g: Relative Performance Vs. Y And Y Thresholds 

200 I I I I 

1 s 

,z 

L-. 

FIXED BASE 

w :: I- = ZlOO0.9 f 

g I ,” MOVING BASE 

5 

‘) 

w 

cl 

K O* 

0.0 

I 

0.03 

I I I 

0.06 0.09 0.12 

ti T!-lRESHOLD (degrees) 

I 

0.0 

I I I I 1 

0.12 0.24 0.36 0.48 

6 THRESHOLD (degreeshec) 

Figure 5.6h: Relative Performance Vs. $ And I) Thresholds 

-lOO- 
better than predicted. It should be noted however, if many of the 

thresholds are in error their composite effect may be significant. 

Figure 5.7 shows the sensitivity of relative performance to 

changes in observation noise ratio , with all other parameters fixed 

at their nominal values. It is reasonable to conjecture that 

observation noise/signal ratio is affected by the scene 

content-- the more realistic and compelling the scene, the greater 

the motivation and attention and the lower the noise ratio.* It 

can be seen from the plots of figure 5.7 that the "knees" of the 

sensitivity curves are approximately at the nominal noise/signal 

ratios Thus, lower increased noise rates would not result in 

performance predictions much better than those for the nominal 

value, but larger noise ratios could degrade performance 

substantially. The nominal value of noise/signal ratio (-20 dB for 

full attention to the combined longitudinal, lateral task) is 

typical of the values measured in laboratory tracking tasks with 

symbolic displays. One might expect a real world scene to increase 

pilot motivation leading to a lower noise ratio and marginally 

better performance than predicted by the nominal. However, if the 

computer generated scene is impoverished in any significant way, it 

----- * Alternatively, .-- -. the finer--the-detail of the scene, -- the less 

necessary it is to share attention among different parts of the 

scene in order to obtain the same quality of information--thus, 

effectively, reducing the overall noise ratio. 

-101\ 
-11 -14 -17 

NOISE/SIGNAL RATIO (dB) 

-20 -23 

L I I I I I iI a 

0.125 0.25 0.5 1.0 2.0 

ATTENTION TO TASK 

Figure 5.7a: Relative Performance Vs. Observation Noise Ratio 

(CH-47 Longitudinal Axis) 

-102- 
60 

50 

0 

-10 

-71 -14 -17 

NOISE/SIGNAL RATIO (dB) 

-20 -23 

Iii a 

0.125 0.25 0.5 1.0 2.0 

ATTENTION TO TASK 

Figure 5.7b: Relative Performance Vs. Observation Noise Ratio 

(CH-47 Lateral Axis) 

-103- 
may not provide the required control information in a more precise, 

inteqrated or attention-getting fashion than a symbolic display; if 

this were the case, relative performance could be affected 

severely. 

Before leaving this discussion of the effects of individual 

CGI parameters, it should be noted, as a caution, that the 

assumption of a one-to-one correspondence with model parameters is 

made for simplicity. In reality, design changes can alter several 

factors related to information processing and tradeoffs are often 

the result. For example, improved scene content may lower 

noise/signal ratios but may require more computation and, hence, 

increase delay. 

5.3.3 Effects of VMS Parameters 

Only two aspects of the motion sys tern were examined 

independently, VMS platform bandwidth and the presence or absence 

of a surge motion washout filter. Relative performance is plotted 

as a function of platform bandwidth and control task in figure 5.8. 

A bandwidth of zero corresponds to a fixed base configuration and 

an infinite bandwidth corresponds to flight motion. It can be seen 

that changing the bandwidth does not have an appreciable effect on 

relative performance, so long as a reasonable degree of motion 

fidelity is maintained. The effects of bandwidth are somewhat more 

a-104- 
BANDWIDTH - 0 ‘h NOMINAL NOMINAL 0 ‘/I NOMINAL NOMINAL 

LONGtTUDlNAL AXIS LATERAL AXIS 

Figure 5.8: Relative Performance Vs. VMS Platform Bandwidth 
pronounced for the longitudinal control task than for the lateral. 

It was desired to explore the possibility of using the optimal 

control model to design a washout filter for the VMS and to examine 

the effect of such a filter. An examination of the rms hover 

errors for the nominal conditions showed that only the VMS limits 

in surge would be exceeded a reasonable fraction of the time. 

Thus, only a surge washout filter was designed using the model. 

The design methodology used and the resulting filter are described 

in Appendix B. 

Performance predictions with and without a surge washout 

filter are presented in table 5.7. The scores correspond to 

measures at the output of the motion system. The washout filter 

keeps the motion system essentially fixed in surge in this case. 

However, all other platform motions are within 10% of those that 

would have been obtained without a washout filter. The effect of 

the washout filter on overall performance is to introduce about a 

3% degradation in J. Thus, the washout filter appears to have 

satisfied reasonable design objectives for such a filter (see 

discussion in Appendix B). 

5.3.4 Effects of Vehicle Dynamics 

The effects of simulator parameters will depend on the 

specifics of the task, including the vehicle dynamics. This has 

-lOG- 
Table 5.7 EFFECT OF,SURGE rJASHOLJT ON 

RMS PLATFORM MOTIOI< (CH-47 LONGITUDINAL AXIS) 

VARIABLE UNITS V-MS LIMITS WITH WASHOUT W/O WASHOUT 

FT 

FT/SEC 

FT 

FT/SEC 

DEG 

DEG/SEC 

INCHES 

INCHES 

+2.5 

22.0 

230.0 

+20.0 

226.0 

215.0 

-- 

7.987 7.73 

0.0 5.14 

0.0 0.98 

1.92 1.91 

0.636 0.66 

0.801 0.87 

0.913 0.91 

0.23 0.23 

0.14 0.15 

-I- PREDICTED EMS MOTION 

already been illustrated in differences between predicted 

longitudinal and lateral performance. To explore further the 

effects of vehicle dynamics, results were obtained for the CH-47 

,-107~. 
with a velocity command control augmentation system, as specified 

in Hoffman et al (1976) . The augmented vehicle presents a 

significantly less difficult control task. Figure 5.9 gives 

relative performance as a function of control augmentation for the 

nominal simulator configuration (and for the nominal fixed-base 

configuration). The effect of simulator characteristics is 

substantially less for the augmented vehicle. However, the effect 

is still significant for longitudinal control and for fixed-base 

simulation of lateral (augmented) control. 

It is interesting to ask how simulator characteristics might 

bias the results of an investigation into the effectiveness of the 

control augmentation design. This can be examined by comparing the 

ratio of augmented to unaugmented performance predicted for flight 

with that predicted for various simulator configurations. An 

unbiased evaluation would yield the same ratio for a simulator as 

for flight. The predicted ratios for flight, nominal moving-base 

and nominal fixed-base configurations are shown in figure 5.10. It 

can be seen that the simulators tend to predict a greater advantage 

for control augmentation than is predicted for flight. However, 

the differences in this case are small, and results suggest the 

overall improvement from augmentation would be apparent in either a 

motion-base or fixed-base simulator. 

-108- 
w 

> 

F 

4 

k! 20 

0.0 

NO CONTROL AUG. 

VELOCITY COMMAND 

CONTROL AUG. 

M.B. F.B. M.B. F.B M.B. F.B. M.B. F.B. 

.--+ / 

LONGITUDINAL AXIS LATERAL AXIS 

Figure 5.9: Relative Performance Vs. Control Augmentation 
1 

0 

FLIGHT MOVING BASE FIXED BASE FLIGHT MOVING BASE FIXED BASE 

SIMULATOR SIMULATOR SIMULATOR SIMULATOR 

LONGITUDINAL AXIS LATERAL AXIS 

Figure 5.10: Performance Ratio Vs. Simulator Configuration 
6. SUMMARY AND CONCLUSIONS 

The optimal control model for pilot/vehicle analysis has been 

used to explore the effects of a CGI visual system and motion 

system dynamics on helicopter hover simulation fidelity. This was 

accomplished by expanding the perceptual aspects of the model to 

include motion sensing and by relating CGI parameters to 

information processing parameters of the model. Simulator fidelity 

was examined by comparing predicted performance and workload for 

flight with that predicted for various simulator configurations. 

The results of the analysis suggest that simulator 

deficiencies of a reasonable nature (by current standards) can 

result in substantial performance and/or workload infidelity. Both 

CGI and motion system effects are significant for this task. There 

is an interaction between the two sources of pilot cues. In 

particular, the presence of motion reduces the sensitivity to CGI 

limitations. 

With respect to the CGI system, the most important parameter 

in terms of its effect on performance was display delay. This was 

followed in order of importance by display resolution and 

field-of-view. 

The main effect associated with motion system bandwidth was 

introduced by going to a fixed-base configuration. Halving the VMS 

-lll- 
platform bandwidth or going to full flight motion made only a 

marginal change in the performance predicted for the nominal VMS 

bandwidths. The task considered violated motion system limits only 

in the surge-axis, and a washout filter was designed for this axis 

using the model. The filter achieved its design objectives by 

keeping surge motions within limits while maintaining other motions 

and overall performance close to their original values. 

The trends of the results are fairly consistent although there 

were some differences between lateral and longitudinal control 

tasks. The magnitude of the effects and relative importance of 

various parameters are clearly dependent on the task as exemplified 

here by longitudinal vs. lateral and unaugmented vs. augmented 

vehicle dynamics. It is, of course, for this reason that models of 

the pilot/vehicle system are needed to evaluate the importance of 

simulator parameters for a given situation. 

Several areas for further research suggest themselves. First, 

it would be desirable to validate some of these results 

empirically. In this regard it should be noted that model 

predictions of other simulator effects have been borne out by 

experimental data (see, for example, Junker and Levison (1978)) as, 

of course, has the basic optimal control model. Nonetheless, 

certain assumptions were necessary concerning the relationship of 

model parameters to CGI parameters and the sharing (or non-sharing) 

-112- 
of attention between visual and motion modalities and these 

assumptions have not been verified. Indeed, a fundamental study of 

these relationships would probably be more valuable in the long run 

than empirical validation of the specific results obtained here. 

Along similar lines, it would be desirable to incorporate in the 

model mechanisms related to other simulator cueing systems such as 

g-suits and g-seats, control loaders, etc. 

Finally, the use of the model to aid in the design of 

compensation for simulator deficiencies is a promising area for 

research. The design of washout filters discussed herein is one 

possibility that seems worthy of further study. Perhaps more 

important would be the development of a model-based methodology for 

designing compensators for simulator delays. 

-113- 
7. REFERENCES 

1. 

2. 

3. 

4. 

5. 

6. 

Baron, S. and J. E. Berliner, "MANMOD 1975: Human Internal 

Models and Scene-Perception Models", Technical Report No. 

RD-CR-76-3, U.S. Army Missile Command, Redstone Arsenal, 

Alabama, September 1975. 

Baron, S. and Kleinman, D. L., "The Human as an Optimal 

Controller and InformationProcessor",N ASA-CR-1151, September 

1968. (Also IEEE Trans. Man-Machine Systems: 

Baron- S. and Levison, W. H., "A Manual 

Analysis of Vertical Situation Displays for 

NASA CR-114620, April 1973. 

MMS-10, 1969.) 

Control Theory 

STOL Aircraft," 

Baron, S., Muralidharan R. and Kleinman, D. L., "Closed Loop 

Models for Analyzing the Effects of Simulator 

Characteristics", AIAA Flight Simulation Tech. Conf., 

Arlington, TX, September 1978. 

Brown, J. L., "Visual Elements in Flight Simulation", 

University of Rochester, TR-73-2, December 1973. 

Gibson, J. J., The Perception of the Visual World, Riverside 

Press, Cambridge, Mass., 1950. 

-114- 
7. ‘Gibson, J. J., et al., "Parallax and Perspective During 

Aircraft Landings", American J. of Psych., 68, 1955. - 

8. Graham, C. H., "Perception of Movement", in Vision and Visual 

Perceptioq, C. H Graham (editor), John Wiley and Sons, N.Y., 

1965. 

9. Gum, D. R. and Albery, W. B., "Time Delay Problems Encountered 

in Integrating the Advanced Simulator for Undergraduate Pilot 

Training", J. of Aircraft_, 4_, 1977. 

10. Hess, R., "Prediction of Pilot Opinion Ratings Using an 

Optimal Pilot Model", Human Factors, 19, 1977. - 

11. Hoffman, W. C., Kleinman, D., and Young, L., "Display/Control 

Requirements for Automated VTOL Aircraft", ASI-TR-76-39, 

October 1976. 

12. Junker, A. M. and Levison, W. H., "Recent Advances in 

Modelling the Effects of Roll Motion on the Human Operator", 

Aviation Space and Environmental Medicine, 49, 1978. 

13. Kleinman, D. L., "Solving the Optimal Attention Allocation 

Problem in Manual Control", IEEE Trans. on Automatic Control, 

AC-21, 1976. 

.-115- 
14. 

15. 

16. 

17. 

18. 

19. 

Kleinman, D. L., Baron, S., and Levison, W. H., "An Optimal 

Control Model of Human Response, Part 1: Theory and 

Validation", Automatica, 6, 1970. 

Kleinman, D. L., Baron, S. and Levison, W. H., "A Control 

Theoretic Approach to Manned-Vehicle Systems Analysis", IEEE 

Trans. on Auto. Control, AC-16 1971. 

Levison, W. H., "The Effects of Display Gain and Signal 

Bandwidth on Human Controller Remnant", AMRL-TR-70-93, 

Wright-Patterson Air Force Base, Ohio, March 1971. 

Levison, W. H., Baron S., and Kleinman, D. L., "A Model for 

Human Controller Remnant", IEEE Trans. onMan-Machine Systems, 

MMS-10, 1969. 

Levison, W. H., Elkind, J. I., and Ward, J. L., "Studies of 

Multivariable Manual Control Systems: A Model for Task 

Interference", NASA-Ames Research Center, NASA CR-1746, May 

1971. 

Levison, W. H. et al, "Proposal for Research in Manual 

Control Techniques Applied to Military ACFT Display Design", 

BBN Proposal P73-BSC-3, Bolt Beranek and Newman, Inc., 

Cambridge, Mass., 1972. 

-116 
20. Lute, R. D. and Galanter, E., "Discrimination", in Handbook of -----IMathematical Psy_chology, R. D. Lute, R. R. Bush, and E. ----- ---- 

Galanter (editors), vol. 1, John Wiley and Sons, Inc., New 

York, 1963. 

21. Matheny, W. G., et al., "An Investigation of Visual, Aural, 

Motion and Control Movement Cues", Naval Training Device 

Center, Technical Report NAVTRADEVCEN 69-C-0304-1, April 1971. 

22. Quiejo, M. J. and Riley, D. R., "Fixed-Base Simulator Study of 

the Effect of Time Delays in Visual Cues on Pilot Tracking 

Performance", NASA TN D-8001, October 1975. 

23. Riggs, L. A., "Visual Acuity", in Vision and Visual -- 

Perception, C. H. Graham (editor), --- John Wiley and Sons, NY, 

1965. 

24. Staples, K. J., "M-tion- Visual, and Aural Cues in Piloted 

Flight Simulation", AGARD Conference Proceedings No. 79 on - -------- ----- 

Simulation, AGARD CP-79-70, March 1970. -- 

25. Thielges, J. R., Matheny, W., "Analysis of Visual 

Discriminations in Helicopter Control", HumRRO Technical 

Report 71-13, June 1971. 

26. Wewerinke, P. H., "Visual Scene Perception Process Involved in 

the Manual Approach", NLR-TR-78130U, 1978. 

-117- 
27. Wewerinke, P. I-I., "A Theoretical and Experimental Analysis of 

the Outside World Perception Process", Proceedings of the --- -.--_ 

Fourteenth Annual Conference on Manual Control, NASA conf. ---------.-__ - --- -- 

Pub. 2060, November 1978. 

28. Young, L. R. and Meiry, J. L., "A Revised Dynamic Otolith 

Model", Aerospace Medicine, 39, 1968. .I 

29. Zacharias, G. L., "Motion Cue Models for Pilot-Vehicle 

Analysis", AMRL-TR-78-2, Wright-Patterson Air Force Base, 

Ohio, May 1978. 

-118- 
APPENDIX A: 

SYSTEM MATRICES FOR THE SIMULATED CH-47 HOVER TASK 

As noted in the main text, dynamics associated with the 

vehicle, gust environment, control tasks, simulator, and human 

sensory systems are augmented together into a single A,B,C,D,E 

equation set for use in the OCM. This appendix presents the 

numerical values of these matrices as used in our analysis. 

However, rather than display them in augmented form, the ABCDE 

description of each system element corresponding to the blocks in 

Figure 2.2 will be given on the following pages. For reference, 

Figure A.1 and A.2 show, in a more stream-lined fashion, the name 

of the dynamics set and the interconnections of each set for the 

unaugmented control, realistic VMS-realistic CGI simulator 

configuration. Notice that any other configuration investigated 

can be recreated by either shunting out sets of dynamics (for 

example, removing PADE3 elements to form the realistic VMS-perfect 

CGI case), or by replacing one set with another (for example 

replacing C47LOB with C47LOF to form the augmented control case). 

-11% 
gust dynamics CGI 

. 

‘ill 

‘lw 

I 

vehicle 

dynamics 

human sensory dynamics 

F 

*I 

- __) OTOLTH 

Figure A.l: Linking Diagram for Nominal Simulator Configuration 

(Longitgdinal Axis) 
gust dynamics ‘I” 

‘IP 

NOISLA 

-uI “cl 

vd 

pg 

%I 

a.9 

6, 

vehicle 

dynamics 

. 

C47LAB lb- I!#- r 1 

11 ‘P.TP 4 PADE 3 Ihuman sensory dynamics 

VMS 

LpGJMTVLA 

4, ;I;, 

‘VEST. 

Figure A.2: Linking Diagram for Nominal Simulator Configuration 

(Lateral Axis) 
NOISLO.DNL 

Dryden gust model for CH-47 longitudinal axis. 

TOTAL NO. NOISE STATES = 4 

A MATRIX: 

-4.7403-02 

0. 

0. 

0. 

B MATRIX: 

1.9263+00 

0. 

0. 

0. 

NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

0. 

0. 

0. 

D MATRIX: 

0. 

0. 

0. 

0. 

EIGENVALUES 

REAL 

1 -4.7403-02 

2 -2.5303-01 

3 -2.5303-01 

4 -2.010E-01 

0. 0. 0. 

-5.060E-01 2.5303-01 0. 

-2.5303-01 0. 0. 

-4.000E-03 2.000E-03 -2.010E-01 

0. 

2.3583+00 

1.3623+00 

1.870E-02 

0. 0. 0. 

l.OOOE+OO 0. 0. 

0. l.OOOE+OO 0. 

0. 0. l.OOOE+OO 

0. 

0. 

0. 

0. 

IMAG FREQ DMPG 

O.OOOE+OO 4.7403-02 l.OOOE+OO 

O.OOOE+OO 2.5303-01 l.OOOE+OO 

O.OOOE+OO 2.5303-01 l.OOOE+OO 

O.OOOE+OO 2.010E-01 l.OOOE+OO 

-122- 
NOISLA.DNL 

Dryden gust model for CH-47 lateral axis. 

TOTAL NO. NOISE 

A MATRIX: 

-9.4803-02 

-4.7403-02 

0. 

1.000E-03 

B MATRIX: 

2.3603+00 

1.3603+00 

0. 

-2.480E-02 

NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

0. 

0. 

0. 

D MATRIX: 

0. 

0. 

0. 

0. 

EIGENVALUES 

REAL 

1 -4.7403-02 

2 -4.7403-02 

3 -2.6803-01 

4 -2.000E-01 

STATES = 0 

4.740E-02 

0. 

0. 

-5.000E-04 

0. 

0. 

1.6503-02 

0. 

0. 0. 0. 

l.OOOE+OO 0. 0. 

0. l.OOOE+OO 0. 

0. 0. l.OOOE+OO 

0. 

0. 

0. 

0. 

IMAG FREQ DMPG 

9.3443-06 4.7403-02 l.OOOE+OO 

-9.3443-06 4.7403-02 l.OOOE+OO 

O.OOOE+OO 2.6803-01 l.OOOE+OO 

O.OOOE+OO 2.000E-01 l.OOOE+OO 

0. 0. 

0. 0. 

-2.000E-01 0. 

0. -2.6803-01 

-123- 
C47LOB.DNL 

CH-47 longitudinal vehicle dynamics for the unaugmented 

control case. 

SYSTEM UNSTABLE 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

0. 

0. 

l.OOOE+OO 

0. 

2.6203+00 

-1.8203-02 

0. 

0. 

0. 

0. 

1.350E-01 

-7.3003-03 

0. 

l.OOOE+OO 

0. 

0. 

-1.230E+OO 

9.5003-03 

B MATRIX: 

0. 

0. 

0. 

1.8203-02 

4.4003-43 

-4.000E-04 

0. 

0. 

0. 

7.3003-03 

-8.1203+00 

2.9903-01 

0. 

0. 

0. 

-9.5003-03 

1.91OE-02 

-1.300E-03 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 0. 

4.000E-04 -3.2203+01 

l.OOOE+OO 0. 

-2.9903-01 0. 

0. 0. 

1.300E-03 0. 

0. 0. 

-2.6203+00 1.170E-01 

0. 0. 

-1.350E-01 1.700E-02 

0. 0. 

1.230E+OO 3.2903-01 

-124- 
NO NOISE INPUTS 

C. MATRIX: 

l.OOoE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

5.7303+01 

0. 

2.6203+00 

0. 

1.350E-01 

0. 

5.7303+01 

0. 

-7.0483+01 

0. 

l.OOOE+OO 

0. 

0. 

0. 

0. 

-1.8203-02 

-7.3003-03 

0. 

5.4433-01 

0. 

0. 

l.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 0. 

l.OOOE+OO 0. 

0. 5.7303+01 

0. 

4.000E-04 

-2.9903-01 

0. 

7.4493-02 

0. 

0. 

0. 

0. 

0. 

-125- 
D MATRIX: 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

1.8203-02 

4.4003-03 

7.3003-03 

-8.1203+00 

0. 

0. 

-5.4443-01 

l.O94E+OO 

EIGENVALUES 

REAL 

1 O.OOOE+OO 

2 -1.405E+00 

3 7.8093-02 

4 7.8093-02 

5 -2.9823-01 

6 -6.9273-09 

0. 

0. 

0. 

0. 

0. 

0. 

-4.000E-04 

2.9903-01 

0. 

-7.4493-02 

IMAG 

O.OOOE+OO 

O.OOOE+OO 

4.5993-01 

-4.5993-01 

O.OOOE+OO 

O.OOOE+OO 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. -2.6203+00 

0. -1.350E-01 

0. 0. 

0. 7.0483+01 

F-Q DMPG 

O.OOOE+OO 1.7013+38 

1.405E+OO l.OOOE+OO 

4.6643-01 -1.6743-01 

4.6643-01 -1.6743-01 

2.9823-01 l.OOOE+OO 

6.9273-09 l.OOOE+OCJ 

0. 

0. 

0. 

0. 

0. 

0. 

1.170E-01 

1.700E-02 

0. 

1.8853+01 

-12G- 
C47LAB.DNL 

CH-47 lateral vehicle dynamics for the unaugmented 

control case. 

SYSTEM UNSTABLE 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

0. 

0. 

l.OOOE+OO 0. 0. 

0. 

8.6003-03 

-1.370E-01 3.2003+01 -1.4903+00 

0. 

0. 

0. 0. l.OOOE+OO 

0. 

7.5003-03 

-6.7003-03 0. -7.2303-01 

0. 

l.OOOE+OO 

0. 

0. 

-4.0903-02 

-1.100E-03 0. -5.4703-02 

B MATRIX: 

0. 

0. 

1.370E-01 

-5.0403-02 

0. 

0. 

6.7003-03 

-3.7503-02 

0. 

0. 

l.lOOE-03 

2.000E-01 

0. 

0. 

0. 

0. 

0. 

0. 

0. 0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 0. 0. 

1.49OE+OO -8.6003-03 l.l60E+oo 

0. 0. 0. 

7.2303-01 -7.5003-03 4.3703-01 

0. 0. 0. 

5.4703-02 4.09OE-02 4.2703-02 

-127- 
NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

5.7303+01 

0. 

8.600E-03 

0. 

0. 

0. 

4.2983-01 

0. 

5.7303+01 

0. 

-2.3443+00 

0. 

l.OOOE+OO 

0. 

0. 

0. 

0. 

-1.370E-01 

0. 

-3.8393-01 

0. 

-6.303E-02 

0. 

0. 

5.7303+01 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

5.7303+01 

0. 

0. 

-1.49OE+OO 

5.7303+01 

-4.1433+01 

0. 

-3.1343+00 

0. 

0. 

0. 

0. 

5.7303+01 

0. 

0. 

0. 

0. 

0. 

0. 

-120 
D MATRIX: 

0. 

0. 

0. 

0. 

0. 

0. 

:: 

0. 

0. 

0. 

0. 

1.370E-01 

-5.0403-02 

0. 

0. 

3.8393-01 

-2.1493+00 

0. 

0. 

6.3033-02 

l.l46E+Ol 

EIGENVALUES 

REAL 

1 O.OOOE+OO 

2 -9.8863-01 

3 6.4923-02 

4 6.4923-02 

5 -4.2143-02 

0. 0. 0. 

0. 0. 0. 

0. 0. 0. 

0. 0. 0. 

0. 0. 0. 

0. 0. 0. 

0. 1.49OE+OO -8.6003-03 

0. 0. 0. 

0. 4.1433+01 -4.2983-01 

0. 0. 0. 

0. 3.1343+00 2.3443+00 

IMAG 

O.OOOE+OO 

O.OOOE+OO 

4.6113-01 

-4.611E-01 

O.OOOE+OO 

6 -7.9103-09 O.OOOE+OO 

F=Q DMPG 

O.OOOE+OO 1.7013+38 

9.8863-01 l.OOOE+OO 

4.6573-01 -1.3943-01 

4.6573-01 -1.3943-01 

4.2143-02 l.OOOE+OO 

7.9103-09 l.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

l.l60E+OO 

0. 

2.5043+01 

0. 

2.4473+00 

-129- 
C47LOF.DNL 

CH-47 longitudinal vehicle dynamics for the 

(system f) augmented control case. 

SYSTEM NEUTRALLY STABLE 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

0. 

0. 

l.OOOE+OO 

8.9003-06 

1.8253+00 

0. 

0. 

0. 

-3.7003-06 

5.9703-02 

-4.9003-03. 

0. 

l.OOOE+OO 

0. 

2.5003-05 

-3.4643+00 

3.0703-02 

B MATRIX: 

:: 

0. 

1.8203-02 

4.4003-03 

-4.000E-04 

0. 

0. 

0. 

7.3003-03 

-8.1203+00 

2.9903-01 

0. 

0. 

0. 

-9.5003-03 

1.910E-02 

-1.300E-03 

0. 0. 0. 

-1.200E-06 -4.000E-04 -3.3373+01 

0. l.OOOE+OO 0. 

-5.000E-04 -5.030E-01 -1.211E-01 

0. 0. 0. 

-2.8003-06 -1.000E-03 -3.2783+00 

0. 0. 0. 

0. -2.6203+00 1.170E-01 

0. 0. 0. 

0. -1.350E-01 1.700E-02 

0. 0. 0. 

0. 1.230E+OO 3.2903-01 

-130- 
NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

5.7303+01 

8.9003-06 

1.8253+00 

-3.7003-06 

5.9703-02 

0. 

5.7303+01 

1.4333-03 

-1.9853+02 

0. 

l.OOOE+OO 

0. 

0. 

0. 

0. 

-l.O70E-02 

-4.9003-03 

0. 

1.7593+00 

0. 

0. 

l.OOOE+OO 

0. 

0. 

0. 

-1.200E-06 

-5.000E-04 

0. 

-1.6043-04 

0. 

0. 

0. 

l.OOOE+OO 

0. 

0. 

-4.000E-04 

-5.030E-01 

0. 

-5.7303-02 

0. 

0. 

0. 

0. 

5.7303+01 

0. 

-l.l70E+00 

-1.211E-01 

0. 

-1.8783+02 

-131- 
D MATRIX: 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

1.8203-02 

4.4003-03 

7.3003-03 

-8.120E+OO 

0. 

0. 

-5.4443-01 

l.O94E+OO 

EIGENVALUES 

REAL 

1 -2.223E+OO 

2 -6.2493-01 

3 -6.2493-01 

4 -7.6133-04 

5 -5.0343-01 

0. 

0. 

0. 

0. 

0. 

0. 

-4.0003-04 

2.9903-01 

0. 

-7.4493-02 

IMAG 

O.OOOE+OO 

2.8963-01 

-2.8963-01 

O.OOOE+OO 

O.OOOE+OO 

6 -9.9663-04 O.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

-2.6203+00 1.170E-01 

-1.350E-01 1.700E-02 

0. 0. 

7.0483+01 1.8853+01 

F-Q DMPG 

2.2233+00 l.OOOE+OO 

6.8873-01 9.0733-01 

6.8873-01 9.0733-01 

7.6133-04 l.OOOE+OO 

5.0343-01 l.OOOE+OO 

9.9663-04 l.OOOE+OO 

0. 

-132- 
C47LAF.DNL 

CH-47 lateral vehicle dynamics for the 

(system f) augmented 

SYSTEM NEUTRALLY STABLE 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

0. 

0. 

-l.OOOE-04 

-3.4873-01 

0. 

0. 

-S.OOOE-05 

1.2463-01 

0. 

l.OOOE+OO 

9.6003-07 

-2.7993+00 

B MATRIX: 

0. 

0. 

1.370E-01 

-5.0403-02 

:: 

6.7003-03 

-3.7503-02 

0. 

0. 

1.100E-03 

2.000E-01 

l.OOOE+OO 0. 0. 0. 

-1.819E-01 2.401E+Ol -8.2063+00 -2.170E-01 

0. 0. l.OOOE+OO 0. 

-2.3903-02 -3.0423+00 -3.2833+00 9.8503-02 

0. 0. 0. 0. 

5.000E-04 6.1203-02 2.7803-02 -1.9723+00 

0. 

0. 

0. 

0. 

0. 

0. 

control case. 

0. 0. 0. 

1.49OE+OO -8.6003-03 l.l60E+OO 

0. 0. 0. 

7.2303-01 -7.5003-03 4.3703-01 

0. 0. 0. 

5.4703-02 4.0903-02 4.2703-02 

-133- 
NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

5.7303+01 

-1.000E-04 

-3.487E-01 

0. 

0. 

-2.8653-03 

7.140E+00 

0. 

5.7303+01 

5.5013-05 

-1.6043+02 

0. 0. 0. 0. 

l.OOOE+OO 0. 0. 0. 

0. 5.7303+01 0. 0. 

0. 0. 5.7303+01 0. 

0. 0. 0. 5.7303+01 

0. 0. 0. 0. 

-1.819E-01 -8.200E+OO -8.2063+00 -2.170E-01 

0. 0. 5.7303+01 0. 

-1.3693+00 -1.7433+02 -1.8813+02 5.6443+00 

0. 

2.8653-02 

0. 0. 0. 

3.5073+00 1.5933+00 -l.l30E+02 

-134- 
D MATRIX: 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

k 

1.370E-01 

-5.0403-02 

0. 

0. 

3.8393-01 

-2.1493+00 

0. 

0. 

6.3033-02 

l-1463+01 

EIGENVALUES 

REAL 

1 -1.3393-03 

z -7.0283-01 

4 -2.060E+OO 

5 -1.3993+00 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

1.49OE+OO 

0. 

4.1433+01 

0. 

3.1343+00 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

0. 0. 

-8.6003-03 l.l60E+oo 

0. 0. 

-4.2983-01 2.5043+01 

0. 0. 

2.3443+00 2.4473+00 

IMAG FmQ DMPG 

O.OOOE+OO 1.3393-03 1 OOOE+OO 

2 2733-01 7.3873-01 9.5153-01 

-2.2733-01 7.3873-01 9.5153-01 

O.OOOE+OO 2.060E+OO l.OOOE+OO 

l.O76E-01 1.403E+OO 9.9713-01 

6 -1.3993+00 -l.O76E-01 1.403E+OO 9.9713-01 

-135- 
PADE3.DNL 

99 msec. pade' delay dynamics. 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

-2.020E+01 

B MATRIX: 

2.0203+01 -l.OOOE+OO 

NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 

-2.0203+01 

D MATRIX: 

0. 0. 

2.020E+01 -l.OOOE+OO 

EIGENVALUFS 

REAL IMAG FREQ DMPG 

1 -2.020E+Ol O.OOOE+OO 2.020E+Ol l.OOOE+OO 

-136- 
VMSl.DNL 

VMS platfbrm dynamics for use in the rotational and surge axis. 

Second order approximation with BW = 1.5 hz., and DR = .7 . 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

0. l.OOOE+OO 

-8.8363+01 -1.316E+Ol 

B MATRIX: 

0. 

8.8363+01 

NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 0. 

0. l.OOOE+OO 

-8.8363+01 -1.316~+01 

D MATRIX: 

0. 

0. 

8.8363+01 

EIGENVALUES 

REAL IMAG F=Q DMPG 

1 -6.5803+00 6.7133+00 9.400E+OO 7.000E-01 

2 -6.5803+00 -6.7133+00 9.4003+00 7.000E-01 

-137- 
VMS2.DNL 

VMS platform dynamics for use in the sway and heave axis. 

Second order approximation with BW = 3.0 hz., and DR = .7 . 

TOTAL NO NOISE STATES = 0 

A MATRIX: 

0. l.OOOE+OO 

-3.5343+02 -2.6323+01 

B MATRIX: 

0. 

3.5343+02 

NO NOISE INPUTS 

C MATRIX: 

l.OOOE+OO 0. 

0. l.OOOE+OO 

-3.5343+02 -2.6323+01 

D MATRIX: 

0 

0. 

3.534E+02 

EIGENVALUES 

REAL IMAG FREQ DMPG 

1 -1.316E+Ol 1.3433+01 1.880E+Ol 7.000E-01 

2 -1.316E+Ol -1.3433+01 1.880E+Ol 7.000E-01 

-138- 
MTVLO.DNL 

Formation of proper vestibular cues from RMS platform 

outputs. (longitudinal axis) 

TOTAL NO. NOISE STATES = 0 

NO STATES 

NO NOISE INPUTS 

D MATRIX: 

0 0 

0. 0. 

0. 0. 0. 0. 0. 

0. 0. 0. l.OOOE+OO 

0. 0. 0. 0. l.OOOE+OO 

0. 0. 0. 0. 

0. 0. 

l.OOOE+OO 0": 0. 

0 5.6203-01 0. 

0. 0. 

0. 0. 

0. 

-139- 
MTVLA.DNL 

Formation of proper vestibular cues from RMS platform 

outputs. (lateral axis) 

TOTAL NO. NOISE STATES = 0 

NO STATES 

NO NOISE INPUTS 

D MATRIX: 

0. 

0. 

0. 

0. 

0. 

l.OOOE+OO 

0": 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

l.OOOE+OO -5.6203-01 0. 

0. 0. 

0. 0. l.OOOE+OO 

0. 0. 

0. 

0. 

0. 0. 0. 

l.OOOE+OO 0. 

0. 0. 0. 

0. l.OOOE+OO 

0. 0. 

0. 

-140- 
OTOLTH.DNL 

Otolith dynamics. 

TOTAL NO NOISE STATES = 0 

A MATRIX: 

-1.8763-01 0. 

1.515E+00 -1.515E+00 

B MATRIX: 

1.8763-01 

0. 

NO NOISE INPUTS 

C MATRIX: 

8.000E+OO -7.600E+OO 

D MATRIX: 

0. 

EIGENVALUES 

REAL IMAG FMQ DMPG 

1 -1.8763-01 O.OOOE+OO 1.8763-01 l.OOOE+OO 

2 -l.SlSE+OO O.OOOE+OO 1.515E+00 l.OOOE+OO 

-141' 
OTOLTH2.DNL 

Simplified otolith dynamics. Essentially a 

low pass filter with break at 1.5152 rad. 

TOTAL NO. NOISE STATES = 0 

A MATRIX: 

-l.SlSE+OO 

B MATRIX: 

1,515E+00 

NO NOISE INPUTS 

C MATRIX: 

1.0003+00 

D MATRIX: 

0. 

EIGENVALUES 

REAL IMAG F-Q DMPG 

1 -1.515E+00 0.000E+00 1.5153+00 l.OOOE+OO 

,-142- 
CNALR2.DNL 

Simplified canal dynamics for the roll axis. 

Filter is TlS/(TlS+l) . 

TOTAL NO NOISE 

A MATRIX: 

-1.6393-01 

B MATRIX: 

1.639E-01 

NO NOISE INPUTS 

C MATRIX: 

-l.OOOE+OO 

1.6393-01 

D MATRIX: 

l.OOOE+OO 

-1.6393-01 

EIGENVALUES 

REAL 

1 -1.6393-01 

STATES = 0 

0. 

0. 

l.OOOE+OO 

IMAG FREQ DMPG 

O.OOOE+OO 1.6393-01 l.OOOE+OO 

-143. 
APPENDIX B: DESIGN OF SIMULATOR CONTROL LAWS 

B.l Overview 

The following design procedure is intended to provide desired 

rotational and specific force cues to the simulator pilot through 

co-ordinated attitudinal and translational motions of a moving-base 

simulator. Command-following simulator drive circuits are assumed 

to have been previously designed and implemented. The objective of 

the design procedure, therefore, is to generate a set of simulator 

commands that best meets the following conflicting objectives: 

a. reproduce perceptual cues related to whole-body motion, 

b. minimize the introduction of "false" cues inherent in 

ground-based motion simulation, and 

C. minimize the probability of driving the simulator to its 

physical limits. 

The design procedure leads to a fixed-form control law 

implementation of the general form shown in Figure B.l for either 

the pitch-surge or the roll-sway axis. Inputs to the control law 

are attitude rate and specific force; outputs are attitude and 

position commands to the simulator drive circuits. 

Each simulator command is implemented as the sum of two 

commands -- one arising from simulated attitude rate, the other 

-144-- 
6 = SIMULATED ATTITUDE RATE 

f = SIMULATED SPECIFIC FORCE 

8 c = COMMANDED SIMULATOR ATTITUDE 

Xc = COMMANDED SIMULATOR TRANSLATION 

Figure B.l: Structure of Fixed-Form Control Laws 

-145- 
from specific force. The structure of the linear elements of the 

system shown in Figure B.l is described later. 

B.2 Description of the Design Procedure 

The design procedure consists of the following steps: 

1. 

2. 

3. 

4. 

5. 

Analysis of the pilot/vehicle system to determine the 

characteristics of the motion cues available to the pilot 

in the actual flight task, as well as the importance of 

these cues to the task of closed-loop control. 

Derivation of low-order analytic approximations to the 

spectra for attitude-rate and specific force predicted in 

Step 1. 

Initial design of simulator control laws using an 

appropriate LQG minimization scheme. 

Selection of parameters of the fixed-form structure of 

Figure B.l to approximate the initial design. 

Pilot-vehicle analysis of the simulated flight task using 

the fixed-form simulator control laws. 

These steps are described briefly below. 

B.2.1 Model Analysis of the Flight Task 

The "actual" flight task is analyzed with the optimal-control 

model (OCM) to predict rms levels and power spectra for important 

-14G- 
system variables, including perceptual variables assumed to be 

available to the pilot as a result of whole-body motion.. This 

analysis includes the prediction of the pilot's allocation of 

"attention" to the various perceptual variables; presumably, 

predicted fractional attention relates directly to the importance 

of a particular perceptual variable with regard to achieving the 

closed-loop control objectives. 

B.2.2 Low-Order Spectral Approximation 

Because of the relatively free-form nature of the pilot 

response strategy generated by the OCM, the order of the equations 

of motion of any system variable is equal to the number of state 

variables required to describe the flight task (including aircraft 

states, "input states", and possible additional states related to 

display dynamics and control augmentation). In practice, however, 

a relatively low-order (say three or four degrees of freedom) 

approximation will provide an adequate representation of the 

spectral characteristics of most any signal of interest. To 

minimize problem complexity, then, a suitable fitting procedure is 

employed to obtain low-order approximations to the attitude-rate 

and specific-force spectra predicted in the initial analysis of the 

flight task. 

-147- 
B~2.3 Initial Control Law Design 

The control-law design problem is diagrammed in Figure B.2. 

The dashed block (simulator control law) indicates the element to 

be designed: remaining blocks indicate various dynamic subsystems 

that constitute the problem description. 

The block labeled "approximate pilot/vehicle system" contains 

low-order analytic approximations to the predicted attitude rate 

and specific force spectra obtained in Step 2. Attitude rate and 

specific force serve as inputs to the control laws and are used for 

predicting perceptual errors as described below. The "simulator 

dynamics" element is assumed to contain dynamical models 

(second-order or higher) for both attitude and translational 

response of the simulator. Outputs of this block include position, 

velocity, and acceleration of simulator attitude and translation, 

as well as specific force generated at the pilot's head location. 

For purposes of this design procedure, we assume that the 

pilot's head is located at the center of rotation. Specific force 

is thus defined as 

fs = ~0.562 es + Gs 

where 

-148- 
- % 

APPRflXIMATF 

----- 1 

% 

* % 

SIMULATOR I 

* q 

CONTROL 

I AWC Lrn..U i Y 

DIMULH ,.‘-- ” -.TOR 

DYNAN IICS * xs 

I 

-. I 

C 

- * 3 

J . . L----s * Xs 

fS 

f 

L 

Figure B.2: Design of Control-Law Design Problem 

-149- 
8 

S 

= simulator attitude, degrees 

*S 

= simulator displacement, feet 

fS 

= simulated specific force, feet/second 

The sign preceding the component due to simulator tilt is positive 

for the pitch-surge axis and negative for the roll-sway axis. 

Part of the design objective is to minimize perceptual errors: 

i.e., the difference between the motion perceptions obtained in 

flight and those obtained in the simulator. In both actual and 

simulated flight, motion variables perceived by the pilot through 

vestibular sensory mechanisms are mediated by the dynamical 

response characteristics of the semicircular canals and otoliths. 

Because we are dealing with linear models, we can compute 

perceptual error as the difference between the perception obtained 

in flight and the corresponding perception obtained in the 

simulator, or as the difference between actual and simulated 

stimulus as mediated by sensory dynamics. As the two approaches 

are equivalent, we choose the latter in order to minimize the 

dynamical order of the problem. 

Design objectives of the control laws are formulated in terms 

of the following quadratic performance index: 

-150*- 
s2 -;-2 z 

JC = 91f s? +?I2 e,+q3 0; 

,2 

+q4 x', + q5 xs 

X.2 

+ q6 xs 

,2 -2 

+ 97 Be + 98 fe 

(B.1) 

The first six terms reflect the physical limitations of the 

simulator; the remaining terms reflect the requirement to minimize 

perceptual error. 

It is suggested that weighting coefficients be chosen on the 

basis of maximum allowable values, or "limits" as follows: 

9 = l/(Limit) 03.2) 

Limits related to simulator outputs are usually given in the 

simulator specifications -- these limits may be used for the 

control-law design procedure. 

Two alternative schemes for selecting the appropriate limit on 

perceptual errors are suggested: (1) set the limit equal to the 

perceptual threshold, or (2) set the limit equal to the perceptual 

threshold normalized by the attention devoted to the motion cue as 

predicted in Step 1. In the latter case, the weighting coefficient 

is computed as 

(B.3) 

e-151- 
where "f" is the fraction of attention devoted to the particular 

motion cue, and "threshold" refers to the effective perceptual 

threshold as determined from an experiment in which the subject's 

only task is to detect the cue under study. 

The rationale for these schemes is that there is no need to 

replicate motion cues to within a tolerance that is lower than the 

subject's ability to detect a cue discrepancy. The question arises 

as to whether the "threshold" used in the design procedure should 

be that associated with a basic perceptual limitation, or an 

"indifference" threshold that reflects the interfering effects of 

concurrent tasks (e.g., continuous control). Normalization of the 

basic perceptual threshold by the attention is a means of 

accounting for the larger indifference threshold observed when the 

subject performs combined detection and control tasks. 

Once the coefficients of the performance index have been 

specified, an appropriate LQG minimization scheme is employed to 

determine the simulator control laws. 

The minimization scheme provides solutions to the estimation 

and control elements of the overall design. These results may then 

be processed to provide transfer functions relating control-law 

output variables (commanded attitude and translation) to input 

variables (simulated attitude rate and specific force). 

-152- 
B.2.4 Fixed-Form Approximation 

Implementation of the specified control laws is aided by 

approximating the results of the previous step in the design 

procedure by a suitable fixed-form structure. If the structure of 

Figure B-1 is adopted, the following procedure is to be followed: 

1. Manipulate the LQG solution to yield Bode plots of the 

following transfers: 8,./6, xc: 8 where 

0 

,/e,. ' 

8 Cf 

/f, andxc/e 

f Cf' 

the quantities appearing in these ratios are defined in Figure 

B.1. 

2. Select the parameters of the transfer elements of Figure B-1 to 

provide a best match to the corresponding Bode plots. Various 

automated matching procedures are available to help at this 

stage, although "eyeball" fits to the model-predicted curves 

may be adequate in many cases. 

The following structures were adopted for the four linear 

transfer elements in this study: 

. Tg,e= 

K1 

s .+ al 

T&x=-;:": b1)2 

2 

K2 a2 

Tf,B L.562 (s + a212 

-562 

s + 2a2 

Tf,R 2 

a2 

s + b2 

--153- 

(B.4) 
where attitude is in degrees, translation in feet, specific force 

in g's, and g/57.3 = -562. 

With this scheme, attitude command is implemented as a 

first-order low-pass filter operating on attitude rate (in effect, 

a first-order washout on attitude) plus a second-order low-pass 

filter operating on specific force. Commanded translational 

position is generated by a second-order low-pass filter operating 

on the attitude-related of the commanded simulator attitude plus a 

pole-zero filter operating on the specific force-related portion of 

the attitude command. 

The structure described here has the following properties: 

1. 

2. 

3. 

4. 

Attitude rate commands are transmitted without phase errors at 

high frequencies. 

False tilt cues induced by attitude-rate following are 

eliminated at high frequencies. 

Specific force cues are provided by simulator tilt at low 

frequencies and by translational acceleration at high 

frequencies. 

Phase errors between commanded and theoretical specific force 

are eliminated asymptotically at low and high frequencies. 

-154- 
5. Attitude rate and specific force cues are not necessarily 

reproduced with unity gains. 

Other models may be constructed to have these properties, and 

one might choose to implement a second-order washout instead of a 

first-order washout on attitude commands. This one was selected as 

the structure of minimum complexity required for simulating hover 

motions. 

B-2.5 Model Analysis of the Simulation 

Pilot-in-the loop analysis is performed. This analysis is 

similar to that performed in the first step, except that the pilot 

is now assumed to be operating the simulator with the control laws 

designed as described above. 

If predicted performance scores and pilot response parameters 

are essentially identical to those predicted in step one, the 

control laws may be assumed to provide the pilot with essentially 

that same cues that he would obtain and use in actual flight. If 

predicted performance differs substantially from that predicted 

earlier, one or more of the following problems may exist: (a) the 

fixed-form structure of Figure B-1 is inadequate, (b) cost 

coefficients or other model parameters have been inadequately 

chosen, or (c) the physical constraints of the moving-base 

simulator prohibit a faithful presentation of motion cues to the 

pilot. 

-155- 
Potential inadequacies of the fixed-formcontrol-law structure 

may be explored by re-analyzing the task with a higher-order 

implementation of the control-laws obtained in Step 3 (or with a 

control law obtained from an alternative procedure). In addition, 

new control-law designs may be obtained with alternative choices 

for model parameters. Typically, one would vary the weighting 

coefficients of the performance index to obtain a different trade 

off between perceptual fidelity and simulator travel. If these 

procedures do not lead to an overall improvement in predicted 

performance, one may reasonably conclude that performance 

degradation imposed by the simulator constraints cannot be entirely 

overcome. 

B.3 Design of Pitch-Surge Control Law 

The design procedure described above was employed to design a 

control law for the pitch-surge axis for the helicopter hovering 

task. Two sets of control laws were obtained: one based on 

perceptual indifference thresholds obtained by dividing perceptual 

thresholds by predicted attentional levels, the other based on 

unmodified perceptual thresholds. 

Application of the design procedure deviated from that 

described above in the following respects: 

-156- 
1. The influence of vestibular sensory dynamics was neglected. 

That is, perceptual errors were assumed to be essentially the 

same as simulator errors. 

2. A ninth term was added to the performance index of (B-1) to 

include a specific penalty on attitude-acceleration perceptual 

errors. 

The results of analysis performed previously for the 

assumption qf perfect motion cues (described in the main text) were 

used to obtain low-order spectral representations of simulated 

pitch attitude and specific force along the x axis. "Limits" on 

simulator motion obtained from the simulator specifications yielded 

performance weighting coefficients as defined by (B-2). Table B.l 

shows thresholds (limits) assumed for perceptual variables. 

"Attentions" used in computing indifference thresholds were 

obtained from the preceding analysis and are also shown in the 

Table. Table B,l 

Perceptual Thresholds 

Variable Attention 

Effective,Threshold 

Perceptual I Indifference 

-157- 
The "display vector" used in obtaining the model solution 

consisted of simulated (i.e., theoretical) pitch rate, pitch 

acceleration, and specific force , plus the six simulator variables 

included in the performance index of (B.l). (Perceptual error 

variables, while needed for computing the performance index, were 

not included in the display vector because they would have 

represented redundant variables.) Time delay was set to zero, 

motor time constant and motor noise/signal ratios were made 

negligibly small, and observation noise/signal ratios were set to 

-30 dB for all quantities in the display vector. 

Values for the parameters of the linear transfer elements as 

defined in (B.4) are given in Table B.2. Analysis with the larger 

perceptual thresholds (i.e., perceptual thresholds weighted 

inversely by attention) yielded x-axis simulator commands that were 

small enough to be neglected. Accordingly, no values are given for 

x-axis drive parameters for the control law based on indifference 

thresholds. 

-158 - 
Parameter 

Table B.2 

X-Axis Simulator Drive Parameters 

Effective Threshold 

- 

Perceptual 

- 

1.0 

0.3 

0.1 

1.0 

1.0 

0.7 

Indifference 

1.0 

0.3 

--- 

0.75 

0.62 

--- 

-159-. 
1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. 

NASA CR-3312 

4. Title and Subtitle 5. Rsoort Date 

Pilot/Vehicle Model Analysis of Visual and Motion OCTOBER 1980 

Cue Requirements in Flight Simulation 6. Performing Organization Coda 

7. Author(s Sheldon Baron, Roy Lancraft, and 

Greg Zacharias 

- 

8. Performing Organization Report No. 

4300 

10. Work Unit No. 

9. Performing Organization Name and Address Bolt, Beranek and Newman, Inc. 

50 Moulton Street 

Cambridge, Massachusetts 02138 

12. Sponsoring Agency Name and Add&s 

National Aeronautics and Space Administration 

Washington, D. C. 20546 

15. Supplementaw Notes 

Ames Technical Monitor: Frank Crane 

Final Report 

16. Abstract 

The optimal control.model (OCM) of the human operator is used to 

predict the effect of simulator characteristics on pilot performance 

and workload. The piloting task studied is helicopter hover. Among 

the simulator characteristics considered were (computer generated) 

visual display resolution, field of view and time delay. 

7. Key Words (Suggested by Author(s)1 18. Distribution Statement 

Unclassified - Unlimited 

Flight Simulation, 

Pilot Modelling Star Category - 05 

9. Security Qessif. (of this report) 

Unclassified 

I 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 22. Price’ 

162 A08 

*For saile by the National Technical Information Service, Springfield, Virginia 22161 / 

NASA-Lang1 ey. 1980