Air-to-air tracking experiments were conducted at the Aerospace Medical Research Laboratories using both fixed and moving base dynamic environment simulators. The obtained data, which includes longitudinal error of a simulated air-to-air tracking task as well as other auxiliary variables, was analyzed using an ensemble averaging method. In conjunction with these experiments, the optimal control model is applied to model a human operator under high-G stress

Kleinman, D. L.

Korn, J.

English

NASA Technical Reports Server

MODELING THE EFFECTS OF HIGH-G STRESS ON PILOTS 
IN A TRACKING TASK 
by Jonathan Korn and David L. Kleinman 
Department of Electrical Engineering & Computer Science 
University of Connecticut 
Storrs, Conn. 06268 
SUMMARY 
Air-to-Air trackins experiments have been conducted at the Aerospace 
Medical Research Laboratories (AMRL) using both fixed and moving base (Dynam- 
ic Environment Simulator-DES) simulators. The obtained data, which includes 
longitudinal error of a simulated air-to-air tracking task as well as other 
auxiliar;. variables, was analyzed using an ensemble averaging method. 
In conjunction with these experiments, the Optimal Control Model (OCM) 
is applied to model a human operator under high-G stress. 
INTRODUCTION 
Recent efforts at Aerospace Medical Research Laboratories, WPAFB, have 
demonstrated initial feasibilities of applying the Optimal Control Model [l] 
of human response to the air-to-air tracking problem. 
able to generate predictions of ensemble mean and standard deviations of 
longitudinal tracking error, aircraft stace variables and attained Gz forces 
corresponding t o  arbitrary target profiles. 
were focused on two subproblems. 
weightings and internal model parameter changes to 6-stress were considered. 
Second, a structural change of the model was suggested. 
model development and validation has been generated on the centrjfuge (DES) 
facility at AMRL. The most recent data VS. model comparisons have shown ex- 
cellent correspondance for tracking error ensemble statistics. 
refinement efforts are now under investigation. 
The model has been 
The preliminary modeling efforts 
First, effects that related cost functional 
The data for this 
Further model 
ENGAGEMENT SCENARIO 
Figure 1 shows the geometry of the air-to-air tracking in the longitu- 
dinal plane [ 2 ] .  
i.e. the sight is fixed and aligned with the aircraft body axis. 
al simplification has been added by assuming that pitch angle equals the 
flight path angle. 
In our modeling efforts we assumed no gunsight dynamics, 
An addition- 
55  
https://ntrs.nasa.gov/search.jsp?R=19790007422 2020-03-22T01:34:28+00:00Z
PLANAR ANGLES (LONGITUDINAL) 
= pursuer f p  angle 
%A 
F I G ,  1 : TRACKING GEOMETRY 
= evader f p  angle 0T 
= inertial l ine of sight 'T 
'TA 'A - 'T * relative l ine of sight 
r = 8 - CT = aspect angle T 
OPTIMAL CONTROL MODEL FOR AIR-TO-AIR TRACKING 
The OCM, modified to  treat deterministic target motion assume8 the 
system dynamics 
-.. k(t) - A. - x(t) 3. $u(t) f Fo z(t) 
y w  - co x(t) (2) (1) 
A where u(t)  - is the elevator deflection and 2(t) is  a function of the 
target motion. The state vector is 
- x' * [qT$ qA9 OAr 'A - OTS '1' 
where q ( q  ) i s  the target (attacker) pitch ratar u 
of atte3k 4nd e is the tracking error. 
is the attacker angle 
The obaervationrr are 
y' - [e, is r,  + I '  
56 
with correspondance to the OCM assumtion on observations. 
only a delayed and noisy signal 
The human perceives 
where v ( t )  is a white observation noise with covariance 
Y 
't = operator time delay 
p o  = nominal noise to signal ratio 
fi(t) = fractional attention allocation to the i-th observed variable 
N(ai) = equivalent gain of the visualjindiff erence threshold ai 
yi = mean of y 
a 
Y 
- 
i 
i * standard deviation of y i 
The control input corresponds to the differential equation 
&here L is the feedback gains vector, %(t) i e  the estimated state, IN is the 
neuxo-mhx time constant and v (t) is a white motor noise with covariance 
proportional to the covariance 8f u(t) 
VU(t) = Pu cov[u(t)l, ( 6 )  
p, being the motor noise r a t i o  coefficient. The system matrices axe 
A -  
0 
0 
0 
0 
-1 
0 
- 
O O O Q  
0 0  
0 0  
1 0 0 0  
1 0 V/D 0 
Mq Ma 
2, 
- 
b *  
"0 
57 
0 
Ma 
0 
0 
0 
0 0 0 0 1  
0 1 0 V/D 0 
0 0 0 - 1  1 
1 0 0 V/D 0 - 
The vertical accelerations of the target, and those commanded by the attacker 
are respectively 
GA(t) - " x2(t) + 1 
8 
The constants are 
Ma - 11 
V = 1000 ftlsec 
g = 32.2 ft/sec 
M -7.63 SBC 
D - 1000 f t .  
2 
-1 
q 
Ma - -20'66 sec-' 
aa -2.27 sec -1 
A typical GT time history, used in the present AMRL studies i s  ShQWn i n  Fig. 
2. 
G LEVEL I 
%Ax = 79 
- TIME 
1.59 
-- FIG. 2: TYPICAL % TIME-HISTORY 
58 
The i n t e r n a l  model parameters were set t o  t h e i r  nominal values T - .2 sec,  
m -20 dB, p - -14 dB, fN - .1 sec. Usually, t he  nominal value of p 
(-0' ) f o r  a s i n g l e  observation channel would be -20 dB, 
areY$ observation channels which increase the nominal p 
Y 
Y % Y 
In  our case there  
t o  -14 dB. 
PILOT MODEL RLVXSION AND RESULTS 
Motivated by recent r e s u l t s  i n  modeling A M  t racking under high uncer- 
t a i n t y  131, we write the  human's i n t e rna l  charac te r iza t ion  of t a r g e t  motion 
(x, a qT) as 
ra ther  than 
A l ( t )  - z ( t ) .  
NOW, 
t 9) 
Using t h i s  approach we note the following facts: 
1. a(t)  does not affect  the system model. 
2. a( t )  does a f f e c t  t h e  Kalman f i l ter  subrnodel equation associated 
with t h i s  state,  
The ta rge t  motion is perceived by the  human operator a8 a Markov process a s  
opposed t o  a random walk (U-O), 
perceiving the t a rge t ' s  rncxion. a( t )  is chosen according t o  
It reflects the pursuer 's  uncertainty i n  
where 
The reaul t ing  model-ve-data cowparlions for ensemble man e r r o r  (z(t)) for 
dynamic and static-(; case8 (G-strers and no G-+trees) ora rhoun i n  Figures 
3-4, reapectively.  The agreemante are axcel lant  through the  t r sne ien t  6 
peak t o  recovery. Nominal parameters have been used for  t he  basic QCH 11- 
sponse parameters; the only change batwaan s t a t i c  and dynamic cauee i s  
e 5 3  s t a t i c  
.97 dynamic 'I, 
59 
(14) 
a)  Expertmental Data 
. 
-80 L 
b) Model Predfctions 
F I G .  3: MEAN PITCH TWCKIYG ERROR, 1 PEAK, G STRESS 
60 
20 
1 
CI 
c, 4c
I Y  
-20 
-40 
4 0  
-80 
I I I I I I I t  
20 lo 60 40 /oo /to 
a)  Experimental Data 
- 
L . I I I I t 1 ,  
20 40 60 80 1m 120 
b) Model Predictions 
FIG. 4: MEAN PITCH TRACKING ERROR, 1 PEAK, STATIC G 
61 
CONCLUDING REMARKS 
A preliminary modeling work in the area of air-to-air tracking task has 
been conducted and the initial results have been extremely encouraging. 
ever, further research is needed, and is presently continuing, to interpret 
these results and to 'match the standard deviation data. 
How- 
For modeling work, a major concern is-involved with the OCM internal 
A set of new exper- parameters and their dependence on G, and G, levels. 
iments will be conducted in the near future t o  enhance the observations of: 
this dependence. 
Also, the present model formulation does not include any motion-derived 
cues as G, or G,;.it merely regards these quantities as external stressors, 
and neglects any useful motion cues that they may provide. It is the feeling 
of the authors that this aspect of modeling work need to be considered in any 
future modeling efforts. 
REFERENCES 
1. Kleinman, D.L., Baron, S. and Levison, W.H.: A Control Theoretic Approach 
to Manned-Vehicle Systems Analysis. IEEE Trans. Autom. Control, Vol. 
AC-16, NO. 6 ,  1971. 
2. Harvey, T.R. and Dillow, J.D.: 
Model t o  Air-to-Air Combat. 
August 1974. 
Application of an Optimal Control Pilot 
AIAA Guidance and Control Conference, 
3.  Kleinman, D.L., Ephrath, A . R . ,  and Rao, P. Krishna: Effects of Target 
Motion and Image on AAA Tracking. 
Report TR-77-7, Nov. 1977. 
Univ. of Conn., Dept. of EECS, Tech. 
62 


Modeling the effects of high-G stress on pilots in a tracking task

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