March 2012 

NASA/CR–2012-217555

Intelligent Systems Approach for 

Automated Identification of Individual 

Control Behavior of a Human Operator

Kirill B. Zaychik and Frank M. Cardullo 

State University of New York, Binghamton, New York 
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March 2012 

NASA/CR–2012-217555

Intelligent Systems Approach for 

Automated Identification of Individual 

Control Behavior of a Human Operator 

Kirill B. Zaychik and Frank M. Cardullo 

State University of New York, Binghamton, New York 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

I would first like to extend my gratitude to Professor Frank Cardullo for his support and guidance on 

this research.

I also extend appreciation to Lon Kelly of the NASA contractor UNISYS for his technical support. 

Lon provided key assistance in implementing the large civil transport aircraft software package at the 

Man-Machine Systems Laboratory at SUNY Binghamton and educating me in good programming 

practice.

I would also like to express my appreciation to my colleague Gary George for providing 

experimental data essential to my research. 

I also extend appreciation to Bob Mess in the Man-Machine Laboratory at SUNY Binghamton for his 

help and assistance in ensuring proper functioning of all computer equipment. 

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics 

and Space Administration.


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Figure 2.7. Major operator pathways for compensatory tracking (Hess, [4]) 

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k Kv K: K; :T +
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Figure 2.10. Sample comparison between Hess structural model, precision model and 

experimental data (Hess, [4]) 

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[5] 

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Hess structural model parameters

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output control

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system

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system

Figure 2.12. Block diagram of the descriptive pilot model (Hosman, [10]) 




				

	




	
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frequency response (Hosman, [10]) 

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NL NM NS NZ PS PM PL

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Human control

NL NM NS NZ PS PM PL

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Tracking error
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Human Control Output Tracking Error

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human control activity; b) input reference signal; c) plant output 

 	
	 		 




	
		
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(dotted box) to account for the pursuit nature of the control task (Hess, [4]) 

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Figure 4.11. NASA Langley VMS cockpit view. (Courtesy of NASA Langley) 
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5.2.1. Neural network architecture 

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Table 5.1. Values of parameters of Pre and Post processing for different sets of 

experimental data.

Parameters MCL data set UAV data set

: : DIE a b D:AIC=>E DCCC@IC=E

; ; DIE a b DC<IC@AE DCCC;;IC=E

DI E c d DB=I <B=E 	 D<CCI :=CE 	

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Figure 5.4. Block diagrams of the MATLAB SIMULINK implementation of the ANN used 

to model the testing element of the control behavior: a) overall block-diagram; b) hidden 

layer neuron structure; c) output layer neuron structure

TEC

1

scaling 1

(u+1.2)*0.8/2.6+0.1

scaling

(u+0.3)*0.8/0.5+0.1

postprocessing scaling

(u-0.1)*6/0.8-3

output layer

In1

In2

In3

In4

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hidden layer 

node 2

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hidden layer 

node 1

In1

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node 3

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a10

1

a00

1

tracking error rate of change

2

tracking error

1

Out1

1

sigmoid activation function

1/(1+exp(-u))

Gain 2

w112

Gain 1

w110

Gain

w111

In3

3

In2

2

In1

1

Out1

1

sigmoid activation function

1/(1+exp(-u))

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Gain 4

w211

Gain 3

w210

Gain 1

w213

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4

In3

3

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2

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the Hess structural model. 

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FIR filter added 





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	a
	
	
	




	



	
1

0 0.5 1 1.5 2 2.5 3 3.5

0

500

1000

1500

2000

2500

3000

3500

4000

4500

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	:>"192	


		
	


		

	



		
	




	
	

	

	





	


f 

Out 1

1

Integer Delay 9

 -1

Z 

Integer Delay 8

 -1

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Integer Delay 7

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Integer Delay 6

 -1

Z 

Integer Delay 5

 -1

Z 

Integer Delay 4

 -1

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Integer Delay 3

 -1

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 -1

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Integer Delay 17

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Integer Delay 16

 -1

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Integer Delay 15

 -1

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Integer Delay 14

 -1

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Integer Delay 13

 -1

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Integer Delay 12

 -1

Z 

Integer Delay 11

 -1

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Integer Delay 10

 -1

Z 

Integer Delay 1

 -1

Z 

Integer Delay

 -1

Z 

-K- Gain 7 -K- Gain 8 -K- Gain 9

Gain 6 -K- Gain 5 -K- Gain 4 -K- Gain 3 -K- Gain 2 -K-

-K- Gain 18 -K- Gain 14 -K- Gain 15 -K- Gain 16 -K- Gain 17

-K- Gain 10 -K- Gain 11 -K- Gain 12 -K- Gain 13

Gain 1 -K- Gain -KIn1

1
BC

5.4.Integration of enhancements into selected models of the human 

operator 



	




	
		



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Testing element of 

control modeled by 

ANN

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Figure 5.11. Hosman descriptive model with TEC and Equiripple filter incorporated 

Vehicle 

dynamics

Neuromotor 

Dynamics

Optimal

Feedback 

Gains

Predictor Kalman

Estimator

Time

Delay τ

Neuromuscular noise Observation noise

w t+ ,

u t+ ,

Kr

Testing element of 

control modeled by 

ANN

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model Plant

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optimization

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calculation

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reference 

signal

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control 

signal

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control 

signal

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parameters

Figure 6.1. General architecture of the proposed APID 

6.1.Bit-Climbing Genetic Algorithm 

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Figure 6.2. Bit-Climber Algorithm (BCA) flowchart 

Generate initial 

chromosome

Generate bit-flip schedule

Initiate bit-flipping 

procedure

Evaluate fitness of the new 

chromosome. Determine if 

the new chromosome has 

better fitness

Replace the old 

chromosome with the new 

one

Continue with the bitflipping schedule

Check if the pre-allotted 

number of iterations is 

reached

Return the resulting 

chromosome

Yes

No

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No
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Table 6.1. Standard deviation of identified parameters 

Parameters varied Standard deviation of identified parameters Fitness score

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RF e t+ ,

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WC rate  HI Hc

Figure 6.5. The Hosman descriptive model 

1	+"AA,			

		


			

	
Vm 	

	
Vy 

Vehicle 

dynamics

Neuromotor 

Dynamics

Optimal

Feedback 

Gains

Predictor Kalman

Estimator

Time

Delay τ

Neuromuscular noise Observation noise

RF

+t,

V

Figure 6.6. OCM 



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Table 6.2. Identified parameters of the selected models and their configurations: MCL data 

Hess 

structural 

model

.		






.		



 

.		
.		


	

Ke K: K; Kr w::C X w;:< : lev ; lev < lev @ lev Tn Kn

Original Hess X X X

Hess with 

TEC

X X X X X X X

Hess with EF X X X X X X X X X

Hess with 

TEC and EF

X X X X X X X X X X X

Hosman 

descriptive 

model

.		



	



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.		
.		


	

watt wrate Kr w::C X w;:< : lev ; lev < lev @ lev Tn Kn

Original

Hosman

X X

Hosman with 

TEC

X X X X X X

Hosman with 

EF

X X X X X X X X

Hosman with 

TEC and EF

X X X X X X X X X X

OCM .		


%0/

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.		


Vm Vy Kr w::C X w;:< : lev ; lev < lev @ lev

Original 

OCM

X X

OCM with 

TEC

X X X X

OCM with EF X X X X X X

OCM with 

TEC and EF

X X X X X X X X
>B

Table 6.3. Identified parameters of the selected models and their configurations: UAV data 

Hess structural 

model

.		


.		





.		






.		


	

Ke K: K; Kff Kr w::C Xw;:< : lev Tn Kn

Original Hess X X X X

Hess with TEC X X X X X X X X

Hess with EF X X X X X X X

Hess with TEC 

and EF

X X X X X X X X X

Hosman 

descriptive 

model

.		



	

.		





.		






.		
	

watt wrate Kff Kr w::C Xw;:< : lev Tn Kn

Original Hosman X X X

Hosman with TEC X X X X X X X

Hosman with EF X X X X X X

Hosman with TEC 

and EF

X X X X X X X X

OCM .		
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.		






Vm Vy

Kff Kr w::C Xw;:< : lev

Original OCM X X X

OCM with TEC X X X X X

OCM with EF X X X X

OCM with TEC 

and EF

X X X X X X
>>

6.3.Metrics for evaluation 



	





				

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Figure 6.8. Sample diagram illustrating the benchmark fitness score at 1 produced by the 

precision model as compared to fitness scores of other models 

6.4.Application results. 

		


								


		


		

	
	

		

	





		


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0	B
	

	
	




			

6.4.1. Fitness analysis 

6.4.1.1. MCL data set 








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0 

0.5

1 

1.5

1 2 3 4 

.


%	
	


	-"


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a)

b)

subjects

Fitness

subjects

Fitness
?;

Figure 6.9. Fitness score comparison for a sample group of four subjects form the MCL 

data set: a) Hess structural model, b) Hosman descriptive model, c) OCM 

1		

		
 
	
	+,	




	+-",	


+-0,	"A:C
		

	1
	


 	


				
+	
,

			
	
	


	


	

Figure 6.10. Improvement (percent) of fitness score associated with the addition of an 

equiripple filter (EF) and/or model of testing element of control (TEC). MCL data set 

0

2

4

6

8

1 2 3 4 

.


%	%0/

%0/-"

%0/-0

%0/-"	-0

0 

10

20

30

40

50

60

EF TEC EF + TEC 

Hess 

Hosman 

OCM 

c)

subjects

Fitness

Fitness improvement (%)

Model configuration
?<

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6.4.1.2. UAV data set 





	






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1

2

3

4

1 2 3 4 

.


%	
	


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	-0


	-"	-0

a)

b)

subjects

subjects

Fitness Fitness
?@

Figure 6.11. Fitness score comparison for a sample group of four subjects form the UAV 

data set: a) Hess structural model, b) Hosman descriptive model, c) OCM 

	

		
	





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%	%0/

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c)

subjects

Fitness
?=

Figure 6.12. Improvement (percent) of fitness score associated with the addition of an 

equiripple filter (EF) and model of testing element of control (TEC). UAV data set 

6.4.2. Cross-reference analysis 

	





	
			

	

	


		
5

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10

20

30

40

50

60

70

80

EF TEC EF + TEC 

Hess 

Hosman 

OCM 

Fitness improvement (%)

Model configuration
?A

	
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Figure 6.13. MCL data set: distribution of the model configurations, which resulted in the 

best fit of the actual control behavior 

1		

			


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0 

10

20

30

40

50

60

70

80

Hess Hosman OCM 

Original 

EF 

TEC 

EF + TEC 

Percentage of subject population
?B

Figure 6.14. UAV data set: distribution of the model configurations, which resulted in the 

best fit of the actual control behavior 

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5 

10

15

20

25

30

35

40

45

Hess Hosman OCM 

Original 

EF 

TEC 

EF + TEC 

Percentage of subject population
?>


	


		
	

%0/	





	
	

6.4.3. Statistical Analysis 

	
	

	

		

	

	 #

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,

	


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9

	
	
	

	
	

	
		
	

	

	

1)8	
		


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Figure 6.15. Inter-subject variation of selected parameters of the a) Hess structural model 

b) Hosman descriptive model c) OCM obtained for the MCL data set 

0 

20 

40 

60 

80 

100

120

140

Original EF TEC EF + TEC 

Ke 

K1 

K2 

0 

20 

40 

60 

80 

100

120

140

160

Original EF TEC EF + TEC 

watt 

wrate 

0 

50 

100

150

200

250

300

350

400

Original EF TEC EF + TEC 

Vy 

Vm 

a)

b)

c)

ISV (%) ISV (%) ISV (%)

Model configuration

Model configuration

Model configuration
:CC

	

				
	

		



		


	
#

(		


		
.		

	!: ; K 	K 


	


	
	1)8	

		


	
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1)8

	

		
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wrate 
					
						

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1
	



		

5
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1
		
			



		
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	:C:

Figure 6.16. Inter-subject variation of selected parameters of the a) Hess structural model 

b) Hosman descriptive model c) OCM obtained for the UAV data set 

0 

20 

40 

60 

80 

100

120

140

160

Original EF TEC EF + TEC 

Kff 

Ke 

K1 

K2 

0 

20 

40 

60 

80 

100

120

140

160

180

200

Original EF TEC EF + TEC 

Kff 

watt 

wrate 

0 

50 

100

150

200

250

Original EF TEC EF + TEC 

Kff 

Vy 

Vm 

a)

b)

c)

ISV (%) ISV (%) ISV (%)

Model configuration

Model configuration

Model configuration
:C;

6.4.4. Consistency of results 

1
	







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Table 6.4. Standard deviation (%) of the fitness score as a function of the model 

configuration 

%	-"-0-"R-0MCL data



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6.5.Summary 

1	


				






		
 

		
				

		
	



				

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7. Motion cueing and pilot modeling. 

9
	0		D=E
	 		


	


	

		
	
	



	

	
"B:
	
 	


				
		

	


Ke Cs e	

;

:

;

+ , : k

K

s T 	 

:

:

:

K s

s T 

;

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n

nn n s s



  

Yc

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pn Y

Ym Yf

Yvisual

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s

  s s s Km

;

; ; ; wo wo wo

s

s   !

;

; ; ;

mp

mp mp mp s



  !

Semicircular 

canals

Motion

system

Cueing 

algorithm

Ke Cs e	

;

:

;

+ , : k

K

s T 	 

:

:

:

K s

s T 

;

; ;

n

nn n s s



  

Yc

RF

pn Y

Ym Yf

Yvisual

e ue u: uc

;

+@ @,

+CC> =C:A :,

s

s s



  s Km

;

; ; ; wo wo wo

s

s   !

;

; ; ;

mp

mp mp mp s



  !

Otoliths Motion

system

Cueing 

algorithm

Figure 7.1. Expanded Hess model [5] with motion feedback for a) rotational channel, b) 

translational channel 

a)

b)
:CA


 
		
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7.1.1. Methodology 

		


	
	
		



	


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Figure 7.2. Reference signal in a form of a sum of sinusoids 

; %
	
	

	

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	c u 

Figure 7.3. Man-machine system used to obtain a generic pilot control behavior ( :

s plant 

dynamics) 

0 10 20 30 40 50 60 -4

-3

-2

-1

0

1

2

3

:

s p

crossover

Y

Reference signal Operator Output (frequency sweep)

RF c u e u
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= 0	
	
c u 	cm u 
	

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Figure 7.4. Hess structural model ( :

s plant dynamics). MATLAB SIMULINK block 

diagram 

Visual

In1 Out1

Transport

Delay

To Workspace2

simcontrol _ex

Spindles

K1.s

s+T 1

Saturation Plant

s

1

Neuro Muscular

wn^2

s +2*ksi*wns+wn^2 2

Golgi Tendons

K2

Gain

Ke

From

Workspace1

ref
::C

Figure 7.5. Hess structural model ( :

s plant dynamics) with vestibular system feedback 

path and motion system /washout algorithm models added: a) Translational channel; b) 

Rotational channel 

MATLAB SIMULINK block diagrams 

7.1.2. Results and discussions 


;



	
	


	


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washout

s 2

s +2.04s+1.04 2

platform

s +43.96s+985 2

985

otolith

4s+0.4

0.08s +5.016 s+1 2

Visual

In1 Out1

Transport

Delay

To Workspace 1

simcontrol _ex

Spindles

K1.s

s+T 1

Plant Saturation

s

1

Neuro Muscular

wn^2

s +2*ksi*wns+wn^2 2

Km

Km

Golgi Tendons

K2

Gain

Ke

From

Workspace

ref

Derivative

du/dt

washout

s 2

s +2.04s+1.04 2

platform

s +43.96s+985 2

985

Visual

In1 Out1

Transport

Delay

To Workspace 2

simcontrol _ex1

Spindles

K1.s

s+T 1

Saturation 1

SCC

458 s 2

458 s +85.73s+1 2

Plant

s

1

Neuro Muscular

wn^2

s +2*ksi*wns+wn^2 2

Km

Km

Golgi Tendons

K2

Gain

Ke

From

Workspace1

ref

Derivative 1

du/dt

a)

b)
:::

Figure 7.6. Time histories of the generic pilot control signal produced by the Crossover 

model for: a) :

s plant dynamics; b) ;

:

s plant dynamics 

1	

	
	
	;

:

s 		
	


	
	
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-3

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0

1

2

3

4

5

6

0 10 20 30 40 50 60 -30

-20

-10

0

10

20

30

40

time,sec

a)

b)

Time (sec)
::;

# 	
	


	




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Figure 7.7. PSD of the control behavior signals of the generic human operator simulated by 

the Crossover Model, Hess Structural Model, Hess Structural Model with the motion 

system feedback. Graphs showed for a) longitudinal mode b) lateral mode. Plant dynamics 

:

s

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0

50

100

150

frequency, rad/sec

Crossover

Original Hess

Hess expanded

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

20

40

60

80

100

120

140

160

frequency, rad/sec

Crossover

Original Hess

Hess expanded

a)

b)

Frequency ( 


rad )

Frequency ( 


rad )

Crossover 

Original Hess 

Hess expanded 

Crossover 

Original Hess 

Hess expanded 
::@

Figure 7.8. PSD of the control behavior signals of the generic human operator simulated by 

the Crossover Model, Hess Structural Model, Hess Structural Model with the motion 

system feedback. Graphs showed for a) longitudinal mode b) lateral mode, Plant dynamics 

;

:

s

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0

2000

4000

6000

8000

10000

12000

frequency, rad/sec

Crossover

Original Hess

Hess expanded

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0

2000

4000

6000

8000

10000

12000

frequency, rad/sec

Crossover

Original Hess

Hess expanded

a)

b)

0



%	





 	

0



%	





 	

Frequency ( 


rad )

Frequency ( 


rad )
::=

Table 7.1. Fitness scores produced by the original Hess structural model with (Exp.) and 

without (Orig.) the motion system feedback for different degrees of freedom and different 

plant dynamics models.

Plant 

dynamics

Simulated 

DOF

Model Identified parameters of the model Fitness

Ke K: K; Km

:

s

6	%:C;=@<=;AX C@@@

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6		%:C;=@<=;AX C@@@

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Table 7.2. Landing approach trim conditions. NASA LaRC VMS. 

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Figure 7.9. a) Graphical representation of the wind change pattern during the simulated 

landing approach; b) Side wind acting on the vertical tail and causing the aircraft to roll

)

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Lateral 

component of 

the wind

2wind a 2a 

Roll rate of the 

aircraft due to 

aileron 

deflection

Roll rate of the 

aircraft due to 

side wind

Aileron 

deflection

Aircraftw A a ircraft

Figure 7.10. Block diagram of a pilot-aircraft closed loop control system used to model the 

stabilization task in the roll channel 

7.2.1. Derivation of the aircraft model 

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Table 7.3. Lateral-directional dimensional stability derivatives ( Roskam, [32]) 

;  + , 


y qSC ft Y

m

)

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a

a

l

xx

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p

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r

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mU 


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pl

p

xx

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a

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rl

r

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qSb C L

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Table 7.4. Lateral-directional dimensionless stability derivatives (Roskam, [32]) and 

parameters of the aircraft 

Parameter Value Description (dimensions)

S ==CCO		+ ; ft ,

b :?AO
	+ ft ,

U ;;B8+ 


ft ,

m =A@CCC/	

+slug ,

3 CCC;<>?
+ <

slug

ft ,

xx I A :<B :C '

/
	+ ;

slug f ' t ,

yy

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Figure 7.13. PSD of the actual control signal (blue solid) vs. simulated control signal by 

original Hess (black dotted), Expanded Hess (magenta dot-dashed) and Expanded Hess 

tuned by APID (red dashed). TEC is not present

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.1

0.2

0.3

0.4

0.5

0.6

actual control

original Hess

expanded Hess

expanded Hess tuned

Frequency ( 


rad )

Actual control 

Original Hess 

Expanded Hess 

Expanded Hess tuned 
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Figure 7.14. PSD of the actual control signal (blue solid) vs. simulated control signal by 

original Hess (black dotted), Expanded Hess (magenta dot-dashed) and Expanded Hess 

tuned by APID (red dashed). TEC is present

1
			


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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

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expanded Hess

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Table 7.5. Fitness scores produced by the original Hess structural model with and without 

the TEC model with and without the motion system feedback. 

Model of 

the TEC 

present

Model 

configuration

Identified parameters of the model Fitness

Ke K: K; Km

NO %:A;>;C>X CC@?C

- :A;>;C>:=CCC@><

- P B<<=C?=?BACC;A<

YES %C@::?;;=AX CC<>A

- C@::?;;=A:=CCC<>?

- P C;;<:?;==?CC:=B

- P#.12Km 

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Table 7.6. Identified parameters of the expanded (with motion system feedback) version of 

the Hess structural model with (X) and without (O) TEC. 

Pilot # TEC Identified parameters Fitness

Ke K: K; Km

1 O B<<=CC?=?BA0.0263

X C;;<:?;==?0.0157

2 O <<;;<CB;@A<0.0120

X C<<BA>=:;C0.0048

3 O ;?C?=CA?<:A0.0325

X C;=@C:>;;=0.006

4 O <C>@:BA@:A0.0191

X C>?>:<;=AA@0.0135

5 O =;=AC<;=::0.0368

X CBABB?;C<B0.0108

6 O <B:;>;=A>B?0.0258

X :;??=?;:AC0.0118

7 O ?BA<?C>=:C;C0.0125

X C;?=:?;A<;==0.0054
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8.1.Conclusions 


		
	
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Appendix A. Human operator model block diagrams 

RF

e t+ ,

e t+ , + , j

HC att 

 + , C att r t

& + ,t

 + , C rate r t

HC rate 

WC att 

WC rate  HI Hc

& + ,t

Kff :s e k 	 s

Figure A. 1. Block diagram of the Hosman descriptive model adapted to the pursuit control 

task

Vehicle 

dynamics

Neuromotor 

Dynamics

Optimal

Feedback 

Gains

Predictor Kalman

Estimator

Time

Delay τ

Neuromuscular noise Observation noise

w t+ ,

u+ ,t

Kff :s e k 	 s

Figure A. 2. Block diagram of the OCM adapted to the pursuit control task:<>


			!

:<?

Appendix B. Full set of identified parameters 

Table B. 1. Full set of identified parameters 

Model Identified parameters

Name Bits in the 

chromosome

Identification range

Hess structural model Ke :C, - CI:C;<

K: :C , - CI:C;<

K; :C , - CI:C;<

Hosman descriptive model watt :C , - CI:C;<

wrate :C , - CI:C;<

OCM Vy:C , - CI:C;<

Vm :C , - CI:C;<

Equiripple filter : lev :C , - CI:C;<

; lev * :C , - CI:C;<

< lev * :C , - CI:C;<

@ lev * :C , - CI:C;<

Testing element of control w::C ::, - #:C;<I:C;@

w::: :: , - #:C;<I:C;@
:@C

w::; :: , - #:C;<I:C;@

w:;C :: , - #:C;<I:C;@

w:;: :: , - #:C;<I:C;@

w:;; :: , - #:C;<I:C;@

w:<C :: , - #:C;<I:C;@

w:<: :: , - #:C;<I:C;@

w:<; :: , - #:C;<I:C;@

w;:C :: , - #:C;<I:C;@

w;:: :: , - #:C;<I:C;@

w;:; :: , - #:C;<I:C;@

w;:< :: , - #:C;<I:C;@

Kr :C , - CI:C;<

Remnant Kn :C , - CI:C;<

Tn :C , - CI:C;<

Pursuit tracking gain Kff :<, - CI>:?:

.		
	!	

!				
/06		

:@1

Appendix C. Fitness score comparison histograms: MCL data set 

Hess structural model 

0 

0.5

1 

1.5

1 2 3 4 

.


%	





-"



-0



-"	-0

0 

0.5

1 

1.5

5 6 7 8 

.


%	





-"



-0



-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

9 10 11 12 

.


%	





-"



-0



-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

13 14 15 16 

.


%	





-"



-0



-"	-0

subjects

subjects

subjects

subjects

fitness fitness fitness fitness
:@2

Hosman descriptive model 

0 

0.2

0.4

0.6

0.8

1 

1.2

17 18 19 

.


%	





-"



-0



-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

1 2 3 4 

.


%	
	


	-"


	-0


	-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

5 6 7 8 

.


%	
	


	-"


	-0


	-"	-0

subjects

subjects

subjects

fitness fitness fitness
:@3

0 

0.2

0.4

0.6

0.8

1 

1.2

9 10 11 12 

.


%	
	


	-"


	-0


	-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

13 14 15 16 

.


%	
	


	-"


	-0


	-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

17 18 19 

.


%	
	


	-"


	-0


	-"	-0

subjects

subjects

subjects

fitness fitness fitness
:@

4

OCM 02468

1 2 3 4 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

012345

5 6 7 8 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

0123456

9 10 11 12 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

0 

0.2

0.4

0.6

0.8

1 

1.2

13 14 15 16 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

subjects

subjects

subjects

subjects

fitness fitness fitness fitness
:@5



0 

0.2

0.4

0.6

0.8

1 

1.2

17 18 19 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

subjects

fitness
:@6


			!

:@7

Appendix D. Fitness score comparison histograms: UAV data set 

Hess structural model 

0 

0.5

1 

1.5

2 

1 2 3 4 5 

.


%	





-"



-0



-"	-0

0 

0.5

1 

1.5

2 

2.5

3 

3.5

4 

6 7 8 9 10 

.


%	





-"



-0



-"	-0

subjects

subjects

fitness fitness
:@8

Hosman descriptive model 

0 

0.5

1 

1.5

2 

2.5

3 

3.5

4 

4.5

5 

1 2 3 4 5 

.


%	
	


	-"


	-0


	-"	-0

0

1

2

3

4

5

6

7

8

6 7 8 9 10 

.


%	
	


	-"


	-0


	-"	-0

subjects

subjects

fitness fitness
:49

OCM 

0

1

2

3

4

5

6

7

1 2 3 4 5 

.


%0/	

%0/-"

%0/-0

%0/-"	-0

C

:

;

<

@

=

A

B

>

AB>?:C

.


%0/	

%0/-"

%0/-0

%0/-"	-0

subjects

fitness fitness

subjects
:=C


			!

:=:

Appendix E. Fitness scores. 

	

	
	!
5

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Table E. 1. MCL data set. No enhancements




	%0/

C?BBC@>;C;CBB>B:?@;@;=>C=@=@=;

C?>;C@>==:C>A>>??<=@A<<;?AA=:

C?=;=C:?A;C?@@<@@@AA;<>>B=;;B

CBB@A<?C@=CA:BAB;:C<AB:B@=>;A;

C?AB<<<=C>C?=A<?A=??<;C@;BA;?@

C?><;>@A:BC>@?A?=;>@;AB<<B:<AB

C?>:=>AC;:C?C>:=<A@:@@@?=C@?B?

C?B=@==A=BCBA>CA;>C@;CC:@;<AB;

C?;<:=B:@BC?;A=;@<=>@;=;@<ABA<

C?:@;:><C@C?C>=C=:@B=<::@>><@?

C?B?CC=:=;C?B<<>A@=;;AA><<B=

C;C:C@CBC=C:?<A@;B>?C;:=AC=C;=

C:=B;B@A>=C:=<?==;<<C:@BBA@@CB

CAA:==@=B=CA><;@?B><C=A:>BC@A

C;;<;<;>;AC;;:CACA;=C;>B:>?:A:

CA>C=?C;<<CA??@=:A?BC==?<?;:<<

CA;C;?>?=:CA=;@?@=B<C=@A=@A;?A

C:@C?=?C:C:B<;A@C?<C;C:A::A<<

C:CB>B<@AAC::<C;:>A;C:C>?CC=B<:=;

Table E. 2. MCL data set. Enhanced by EF



R-"
	R-"%0/R-"

C<A>BC@@=C<;:@=>BBBCA??B:==;?

C=:B:C:=B;C@@??>?==@CB>:<::?@@

C=B>:>:<<BC=>?;;>?CAC?;@C<?=>=

C=@@??:C<:C@CB;;=BC:C@>A>=B<A<

C<>;@=B<<@C<B<>AC:;=C?@=CB<@><

CA@C::C>=;C=;>C;;:?:CA;=A=CA=@

CA=;;;AA?AC=?A>CAC=:C><;;:A:B

C=BB>>A>:<C<?:;>C=@ACABA;?==C@

CA?>B@A==;CB@C@?><=>::;?;@C@>

C=?C<;@=<:C=??:>@=<AC?BC@?;B<@

CA;:;C>@=CA<:>;<<=BC?=A@CC:A?

C:==A<>=?=C:;B;:?BC<C;:=AC=C;=

C:;:=A>@><C::>>A=<@<C:@BBA@@CB

C=>B:A?<CAC@>=BC@=C<C=A:>BC@A

C:??@A=;;C;:A<;?:<@C;>B:>?:A:

C=B??B??>;C@A<>=?<=>C==?<?;:<<

C=C?@?@A?>C=CA=:<;;BC=@A=@B:?;

CC?A@CA<<C:@B:A@;BBC:?B:B=B>

CCBACC<?C>CCBAB:<<=:C:C>?CC=B<:=<

Table E. 3. MCL data set. Enhanced by TEC



R-0
	R-0%0/R-0

C=C:@@:?=<C@C<A:<::;CAC;?<;<==

C@B@<;?AA=CA?;AA@>=<C==A;=;=@?

C=;=BB=BC?C@C>=?;>B?C=;>C=@:?A

C@<<<;:AC?C<=AA;:@>AC=:@=B@B=>

C@:<A?=B:BC=<C@><>>;C@B;>>=>BB

CAB=C=C>B@C@>BB:?:@C=@?@C<;<=

C=?C@@=@;<C=A;@C>;<>C=?A<<=:<=

C@C>=BAABBC<?<CC:ABBC=@@BBA>

C@BA@=@C??C=:?;;B=;<CAA@=C<;:

C@?:<;?=@C@=<:A<><;C@>=><<;>@

C<BAC=?=;=C=?:?=C=@:C=>>::=>>?

C::<;<=A:?C::><?<:::C:A@CB>?@;

C:C>@<C?>;C:C?>C<C>@C:<;A@?=<B

C<?>A?>?<:C@:B=>;C<?C=:A<;@=:@

C:A<?;C>A;C:=>=>?CA=C;A>CCA;:;

C<A@B:>:C>C<<B??:>>>C@>><@B?B@

C<<?>;;;;@C<B;C=>::=C@=<AB=C?@

CC?CAC?>=@C::>><C<<C:A?=:=;B=

CCAC;@==>BCCAA<=B:;BCC?CA@;;>::=@

Table E. 4. MCL data set. Enhanced by TEC and EF



R-"R

-0


	R-"R

-0

%0/R-"R

-0

C<;B@<?@BC@=@:=<=@;CA=@:B>:=B

C<C?@?B>?<C=;BBC=A;@CAC=B@:?;=

C<=:>;;?@;C===>B>CA>CA;:;>?:>:

C@:CC@B::?C<@;C???BC@:BB@<>=<

C<ABA?@:<<C=:;@??=<<C=A=CC?B>=

C=:@>@ACAC@:<;?<:@BC==BBC?AB;

C@>C@<C:B;C=<C:=ABABCABBA??<?=

C@CC>><>>=C<>=B;<?;=CA;?:A>::@

C@=>>?=C:C@@<AAC=:=C>>C?;<C>>

C@<BC@?C?=C@@?A?@CA?@>>=:>B

C=<=A;:@?>CA@<;@=B:ACA>AC@BB<=

C:;@>B??@=C::?A>=ABBC;C:=:C@@

CC?CBA>@C=C::C:?B<@BC:<=>B;C?A

C<B<;:A<<BC@@@AB=<<;C=@:@C@>C:

C:??=A:<A@C:AB>CBC;AC;B@A?>;>:

C<;:C<==BAC<>@?<=@=:C@B@@A;>:;

C<:BBA>=>=C<>AC::;<AC@B?C@;@B:

CC>C;;BC>BC::?;?@?@=C:BA:>@CA:

CCA===;>CBCCA>;C?C=CC>B<BA:=:==

Table E. 5. UAV data set. No enhancements. 




	%0/

:@>ABC<:;<:==CA==?A;;:>CAC@ACA

:B@A>?CC@=@AB<C:<;>;==C@<??;A:

:A===?;;:<B=@;:@=>>=<C;:<@:@;

:B=@:=CBB:<;<@A>>A@=A:B@BBC:=?

C?:;<B@:A@:>C:;;@><A<:BB@@=@@

C>B=A@B=AB:=C;CB?>C>;B?CB>B?C>

:@::A;B;CB:@=;A;BBC?;C@<A:?AA;

:::;@B?A@><;>=BB?@C@<?BB<?>@@>

:;:B;CBC;;;=?><><A<?<<;=:<;A=@

<<@:CCC=B@B@=?AC@>>>B>CCB>=@?:

Table E. 6. UAV data set. Enhanced by EF. 

Table E. 7. UAV data set. Enhanced by TEC 



R-0
	R-0%0/R-0

CBB;@A;:;ACB<A;B=;C;::@C:A>:A;

:C<>===?@:;@AC<A=B;;C?:C?>:<:

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Table E. 8. UAV data set. Enhanced by TEC and EF. 



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:=B

References 

D:E
&The Nature of the Operator’s Response in Manual Control, and its Implications 

for Controller Design'(	1--?@.	11*;:?@B

D;E/92O2&Theory of Manual Vehicluar Control'-
=??#A<<

Q:?A?

D<EN	264	)6
O&An Optimal Control Model of Human 

Response. Part I: Theory and Validation'		8A<=B#<A?.	

.

:?BC

D@E

9&A Model-Based Theory for Analyzing Human Control Behavior'	


/	#/	)

9
	8;:;?#:B=:?>=1)4*5C#>?;<;#@AA#G

D=E0	"336		9&Force Cueing Technology Integration and Feedback 

Metrics to Improve DMO Simulator Effectiveness'"9
	6	
	O#

.	
"4%@=@<<;CCA

DAE3390	"/&Application of Neuro-Fuzzy Systems to Behavioral 

Representation in Computer Generated Forces'6!)			:??A

DBE*

)*(
Q)		
64"	!7"$

&Neurocontroller for Lateral Vehicle Guidance'1---/"
	:??<

D>E/92N-&Mathematical Models of Human Pilot Behavior'

392	*:>>:?B@

D?EQ		
!9Q"	Q/&Control Theory for Humans'6	-
	

	


.


;CC<

D:CE
	9&Pilot’s Perception and Control of Aircraft Motions')27


*	
:??A

D::E/92&Advances in Flying Qualities'392

*:=B/	:?>>

D:;E/		9-/92&Experimental Validation and Analytical Elaboration for 

Models of the Pilot’s Neuromuscular Subsystems in Tracking Tasks'*)
:?B:

D:<ET	6Z"$$

	


Z15" QSystem Theory4!*U5

..

:?A=5;?H<?

D:@E33&New Methods of Mathematical Modeling of Human Behavior in the Manual 

Tracking Task'))	7
*U!	4	;CC>

D:=E%

!)&Sieci Neuronowe Do Przetwarzania Informacji'%	O	$	

.!O	
$	
!(O	
$		;CCC

D:AE/Q-&The Effective Number of Parameters: An Analysis of Generalization and 

Regularization in Nonlinear Learning Systems'	
*	1	.



)

@>@B#>=@:??;

D:BE	Q&Adaptation in Natural and Artificial Systems'7
/	

.


:?B=

D:>E3
2-&Genetic Algorithms in Search, Optimization and Machine Learning'

N	.


4
/:?>?

D:?E2	
6&Bit-Climbing, Representational Bias, and Test Suite Design. Proceedings of the 

Fourth International Conference on Genetic Algorithms'7
0		)	

2Q:<#:A:??:

D;CE4	Q3!N&Demo: the Predator UAV Synthetic Task Environment'"

9
	6	
	O	9
	2
AC<C)N)/
	T>=;:;#

ACA:7):=>

D;:E
	9Q0	"/&Motion Cueing Algorithm Development: Human-Centered 

Linear and Nonlinear Approaches'6	9
	0*)09#;CC=#;:<B@B

;CC=

D;;E36&Advanced Techniques for Mitigating the Effects of Temporal Distortions in 

Human in the Loop Control Systems'))	7
*U!	4	

;CC=

D;<ET			
366
O&A Performance Analyzer for Identifying Changes in 

Human Operator Tracking Strategies'
	/	9
	6	
	O#

.	
"4	
%:?B?

D;@EU
!NN-Q/)%/	
	
&Acquisition of Human 

Operator's Skills and Dexterity using Neural Network: Automatic Generation of Neural 

Controller from Chaotic Time Series Data during'0)
)()

8;?;:B#;;;;CC;

D;=E9	
69/0	Q.	!
O&FIR digital Filter Design Techniques Using 

Weighted Chebyshev Approximations'.1---A<:?B=

D;AE9$-U	&Sur la détermination des polynômes d'approximation de degré donnée'

0)/	N	!:C@::?<@

D;BE.	4&A Course in Digital Signal Processing'O1)4*C@B::@?A:A:??B

D;>ET	!N&1)

		1	1	

04			%	'2

	)	7
*U!	

4	;CC?

D;?E

9&Structural Model of the Adaptive human Pilot'Q	3		

08<*=)
H%
:?>C

D<CE

9)	!
O&Assessment of Flight Simulator Fidelity in Multiaxis Task 

Including Visual Cue Quality'Q		8<>*@Q#
;CC:

D<:E03-	9.Q&The Use of Pilot Rating in the Evaluation of Aircraft 

Handling Qualities'*)*2#=:=<:?A?

D<;E9
!	Q&Airplane Flight Dynamics and Automatic Flight Controls'.	19
!	

		-0	:?B?REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Contractor Report

 4. TITLE AND SUBTITLE

Intelligent Systems Approach for Automated Identification of Individual 

Control Behavior of a Human Operator

5a. CONTRACT NUMBER

NNL06AA74T

 6. AUTHOR(S)

Zaychik, Kirill B.; Cardullo, Frank M.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

 NASA Langley Research Center 

Hampton, Virginia 23681-2199 

 

 

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National Aeronautics and Space Administration

Washington, DC 20546-0001

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NASA

13. SUPPLEMENTARY NOTES

 This report was prepared by State University of New York, Binghamton, NY, under NASA contract NNL06AA74T with UNISYS Corporation, Reston, VA. 

 Langley Technical Monitor: Jacob A. Houck

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14. ABSTRACT

Results have been obtained using conventional techniques to model the generic human operator’s control behavior, however little research has been done to 

identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a 

given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One 

enhancement accounts for “testing” control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control 

strategy. This uses the Artificial Neural Network which can be fine-tuned to model the “testing” control. Another enhancement takes the form of an equiripple 

filter which conditions the control system power spectrum . A novel automated parameter identification technique was developed to facilitate the identification 

process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. 

Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned 

Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator 

models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator. 

15. SUBJECT TERMS

Flight simulator; Human operator models; Intelligent systems; Motion systems; Simulator

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