NASA 

TP 

1518 

c.1 NASA Technical Paper 1518 

Computer-Automated Opponent for 

Manned Air-to-Air Combat Simulations 

Walter W. Hankins I11 

SEPTEMBER 1979 

NASA 
TECH LIBRARY KAFB, NM 

0134b85 

NASA Technical Paper 1518 

Computer-Automated Opponent for 

Manned Air-to-Air Combat Simulations 

Walter W. Hankins I11 

Langjey Research Center 

Hampton, Virginia 

National Aeronautics 

and Space Administration 

Scientific and Technical 

Information Branch 

1979 
SUMMARY 

Two versions of a real-time digital-canputer program that operates a 

fighter airplane interactively against a human pilot in simulated air combat 

have been evaluated. They function by replacing one of two pilots in the 

Langley differential maneuvering simulator. Both versions make maneuvering 

decisions from identical information and logic: they differ essentially in the 

aerodynamic models that they control. One is very complete, but the other is 

much simpler, characterizing primarily the airplane's performance (lift, drag, 

and thrust). Both models competed extremely wel against highly trained U.S. 

fighter pilots. Although data from the evaluation indicate that the mre complete model outperformed the pilots to a greater degree than did the simpler 

one, evidence is not sufficient to conclude that the canplete model is inherently superior . 

INTRODUCTION 

In support of the studies in advanced aircraft maneuvering concepts being 

conducted in the Langley differential maneuvering simulator (DMS), Langley 

Research Center has pursued the development of a logic to effectively maneuver 

an airplane in simulated air canbat against a human pilot. This effort has 

resulted in a real-time digital-computer program known as the adaptive maneuvering logic (AMLJ program. The AML can be used to replace one of the DMS pilots 

and to provide the remaining pilot with a tough, competitive, realistic adversary. The AML is canpletely deterministic, yet because its maneuvering is a 

complex interaction between its own present state and that of its opponent's 

immediate past, it minimizes, if not canpletely eliminates, any appearance of 

predictability. In fact, the likelihood of any two AML runs against a human 

pilot being identical is no greater than that of the human pilot identically 

repeating a sequence of maneuvers. 

George H. Burgin of Decision Science, Inc., under contract to Langley, 

first formulated the most basic concepts of the AML and demonstrated their functioning in a batch-processing digital-cmputer program. It was originally 

intended that those batch programs be used to provide preliminary indications 

of the results of studies to be conducted with human subjects in the DMS. However, as the AML development progressed, it became increasingly evident that 

it could be adapted for real-time operation against human pilots. The airplane 

models originally driven by the AML primarily characterized airplane performance 

(lift, drag, and thrust), with minimal modeling of the airplane's rotational 

characteristics. These simplified airplane models were generally considered 

to be acceptable, particularly in light of the more important need at that time 

to enhance the maneuver logic and achieve real-time operation. When real-time 

operation was ultimately achieved, the AML was enthusiastically accepted by 

the fighter pilots who flew against it. However, because the airplane model 

controlled by the pilots was different from that controlled by the AML, a question persisted as to whether the AML could perform maneuvers that the pilots 

L-12768 
could not. Thus, a new effort was begun to develop a control system which would 

allow the AML to control the same airplane model controlled by the human pilots 

in the Dm. The magnitude of this new program would have far exceeded the capacity of the already overburdened Control Data series 6600 canputer which was 

operating the combined AML-DMS program. Fortunately, the more powerful Control 

Data CYBEEt series 175 ccmputer became available at about the same time the new 

program was ready and was able to easily handle it. 

With the new "control model" available, it was desirable to canpare it with 

the older "performance model" since a famrable comparison would increase confidence in the validity of results obtained in previous studies which had used 

the performance model. It would also help justify the continued use of performance models for most applications, Their continued use is desirable because 

they are much simpler and more readily programmed for additional airplanes. 

Presently, only the F-4 airplane has been fully modeled with a control system 

for operation with the AML. 

During late January 1978, four current US. Air Force and U.S. Navy fighter 

pilots flew against an improved maneuver logic that controlled both a performance model F-4 and a control mode1 F-4. It is the primary purpose of this 

paper to report the results of that study. The data analysis will attempt to 

assess the maneuvering effectiveness of both the AML models relative to current 

U.S. military fighter pilots and indirectly to each other. 

Detailed descriptions of the AML and associated airplane models may be 

found in references 1, 2, 3, and 4. This paper will not attempt to repeat: the 

information contained in these publications, but will merely summarize in the 

body of the paper some of the more basic concepts. It will use two appendixes 

to provide more in-depth explanations of previously unpublished modifications 

and improvements made to the AML. Appendix A, entitled "Performance-Model 

Modifications," was authored by Bobby J, Glover of Kentron International, Inc., 

whose efforts have constituted an invaluable input to Langley's AML development 

work. Appendix B describes real-time implementation and refinement of the AML 

control model . 

an,c commanded normal acceleration, g mi ts (1 g = 9.8 m/sec2) 

b wing span, m 

CD drag coefficient 

CL lift coefficient 

*L 

'La aa = -, per degree 

CZ rolling-moment coefficient 

2 
aCZ = -, per degree aB 

rolling moment due to aileron deflection 

rolling moment due to spoiler deflection 

pi tching-moment coefficient 

= -, per degree 

aq 

thrust command, N 

acceleration due to gravity, m/sec2 

altitude, m 

moment of inertia about X body axis, kg-m2 

Mach number 

roll, pitch, and yaw angular velocities, respectively, rad/sec 

dynamic pressure, N/m2 

wing area, m2 

time, sec 

components of velocity along X, Y 

Wsec 

weight 

coordinates of inertial axis system 

body axes 

angle of attack, deg 

angle of sideslip, deg 

and Z body axes , respectively, 

aileron, elevator, and rudder deflections, respectively, deg 

spider deflection, deg 

of f-boresight angle, deg 

bank angle, deg 

3 
4C bank-angle command, deg

$,e,@ Euler yaw, pitch, and roll angles, respectively, of body axis system 

$? e I@ Euler yaw, pitch, and roll angles, respectively, of maneuver plane 

referenced to inertial axis system -" 

axis system referenced to inertial axis system 

Abbreviations: 

Am air-canbat maneuvering 

AML adaptive mane uve ring log i c 

AMLS adaptive-maneuvering-logic score 

DMS Langley differential maneuvering simulator 

MMP moving-maneuver-plane axis system 

TOA time on offense with advantage 

A dot over a symbol indicates derivative with respect to time. 

LANGLEY DIFFERENTIU MANEWERING SIMULATOR 

The AMG program operates in real time on the DMS which has been in operation for about 8 years. It permits two pilots to maneuver their simulated airplanes interactively and has been used extensively in performance evaluations 

of fighter-type airplanes in a one-on-one environment. 

Figure 1 is a diagram of the DMS. It consists of two 40-foot-diameter projection spheres, with cockpits located in such a way as to position the pilot's 

eye at the center of the sphere. The projection equipment, which is located 

above the rear of the cockpits, projects images of the sky, Earth, horizon, and 

opposing aircraft onto the spheres. The resulting projected images move in 

proper perspective for each of the two pilots. The state of the pilot's aircraft is revealed through cockpit instrumentation, visual displays, and secondary cues such as g-suit, blackout, altitude warning, buffet, and thrust 

noise. 

Figure '2 is a block diagram illustrating the operation of the DMS with two 

pilots. The human pilot reacts to information displayed in his sphere and outputs stick, rudder-pedal, and throttle commands. These are transmitted to the 

equations of motion which compute the state of the aircraft corresponding to them. The equations of motion consist of both force and moment equations that 

use tabulated aerodynamic derivatives which define the aircraft being flown. 

The two aircraft may be the same or dissimilar. The relative states of the 

aircraft are canputed, and that information is used to drive the display systems in the spheres. Additional information may be obtained from reference 5. 

4 
The AML programs are digital-canputer programs which interface a guidance 

law for interactive air-combat maneuvering with computer models of fighter airplanes. These programs may act as real-time opponents for human pilots in the 

DMS or they may control two math-modeled airplanes against each other in an offline batch-processing mode. The programs are deterministic, giving their users 

ready access to the causes of all actions taken by the AML. 

The logic for selecting maneuvers is common to all the AML programs. However, two different types of airplane math models are used with this logic. 

They are called the performance model and the control model. The differences 

between these two forms of the AML program will be illustrated by comparing and 

contrasting how each of them operates when used in conjunction with the DMS. 

Figure 3 illustrates how the DMS works when a pilot flies against the AML 

performance model. The human-pilot loop is exactly as it was in figure 2. The 

block labeled air-combat maneuvering (Am) logic is the heart of the AML program. This logic will be described later. Note that two basic simplifications 

have been made with respect to the human-pilot loop. First, the ACM logic outputs bank-angle and load-factor commands directly to the equations of motion 

rather than stick and rudder-pedal displacements. Second, rather than using 

moment equations, the canputer-controlled airplane is driven directly in attitude through its body rates. Filters and rate limits are used to insure smooth 

transitions. Because the airplane is modeled primarily through its performance 

characteristics (lift, drag, and thrust), this form of the AML program is called 

the performance model. 

A second form of the AML program, called the control model, contains a more 

canprehensive airplane model (fig. 4). Note now that the equations of motion 

in the AML loop are identical to those in the human-pilot loop. What is different is the addition of a control system to convert the AMI, bank-angle and loadfactor commands to control surface commands. Because of the time required and 

the difficulty of designing this control system, the control-model form of the 

AML has to date been assembled only for the F-4 airplane. This model enjoys 

the advantage of greater user confidence in the realism and accuracy of the 

simulation. It also, in contrast to the performance model, eliminates the 

question of whether the canputer-controlled airplane can perform maneuvers that 

the pilot, at least in theory, cannot perform. 

Rather than use classical maneuvers such as a high-speed yo-yo, the AML 

uses simpler, more elemental maneuvers. These elemental maneuvers consist of 

segments of circular flight paths lying in planes. The plane curve is specified 

by the throttle setting and the percentage of available load factor. The bank 

angle is chosen such that the curved path remains in the desired plane. The 

airplane continues to fly in the specified plane until a new maneuver is chosen. 

When this occurs, the airplane transitions to a new plane of flight by rolling 

until its wings are properly alined to cause all net forces to lie in the new 

plane. The flight then continues. Figure 5 illustrates how several elemental 

segments may combine to form a three-dimensional space curve. 

5 
TO maintain flexibility in reevaluating the tactical situation for possible 

manuever changes, the time interval between maneuver decisions must be short. ' 

The AML uses l-sec intervals. When the elemental maneuvers are put together, 

they quite often resemble classical maneuvers. 

Maneuver Selection Logic 

Figure 6 is a simplified flow chart of the AML program. The maneuverselection logic is the section of the chart contained between the dashed lines. 

The program uses a fixed logic to decide which maneuver to make next. The 

concept is very simple. The program predicts the future state of its opponent 

over a short time interval. It then canputes what its state would be if it performed each of several optional maneuvers over the same time period. The computed state of each trial maneuver is canpared with the predicted state of the 

opponent, and the most promising trial maneuver is chosen as the next to be 

performed. 

When the program makes maneuvering decisions, it evaluates several standard maneuvers, depending upon its assessment of its current tactical situation. 

The maneuvers being discussed here are those considered applicable to a "normal 

situation." The methods employed to handle special problems such as ground 

avoidance and eErgy management will be discussed later. Under normal circumstances the program evaluates some or all of the following maneuvers: (1) continuing the maneuver currently being performed, (2) a maximum load-factor turn 

in the current plane, (3) maximum load-factor turns in planes banked 100 to 

either side of the current plane of flight, ,(4) flying straight, (5) maximum 

load-factor turns in the "intercept maneuver plane" (the intercept maneuver 

plane is defined as the plane which contains the current velocity vector of 

the canputer-controlled airplane and the predicted position of the opposing airplane) , (6) maximum load-factor turns in planes banked 1 Oo to either side of 

the intercept maneuver plane, (7) maximum load-f actor turns in planes banked 

90°, 1 80°, and 270° from the intercept maneuver plane, and (8) a turn with a 

load factor canputed to yield a circular flight path in the intercept maneuver 

plane which will exactly intercept the opponent at his predicted position. The 

AML extrapolates the opponent's position (and other state variables) at the end 

of a preselected time interval, nominally 2 sec, from the current time using a 

second-order curve containing the opponent's present and two previous positions. 

For each trial maneuver, the program computes what the state of the airplane under its control would be at the end of 2 sec. The canputed state variables resulting from each trial maneuver are then compared with the predicted 

state variables of the opponent by means of a set of questions which results 

in a numerical score, and the maneuver with the highest score is chosen as the 

next to be performed. 

Scoring Trial Maneuvers 

The following 14 questions are those used to obtain the numerical score 

for each trial maneuver. The questions are worded in such a way that they can 

6 
be answered either "yes" or "no." A yes answer is considered good and is 

assigned a value of 1 . A no answer is assigned a value of 0. 

Answers 

1. Is opponent ahead of attacker? 

2. Is attacker behind opponent? 

3. Can attacker see opponent? 

4. Is opponent uable to see attacker? 

5. Is attacker in certain cone behind opponent? 

6. Is opponent not within certain cone behind attacker? 

7. Can attacker fire at opponent? 

8. Can opponent not fire at attacker? 

9. Are both answers to questions 1 and 2 yes? 

10. Is attacker closing on opponent? 

11. Is attacker's predicted altitude greater than 91.44 m? 

12. Are both the answers to questions 3 and 4 yes? 

13. Is attacker's line of sight less than 60°? 

14. Is attacker's line of sight zero or decreasing? 

SCORE 

aA "yes" receives a zero; a "no" receives -13. 

Yes 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

a0 

1 

1 

- 1 

13 

No 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

a-1 3 

0 

0 

0 

-1 3 

I 

Although stated here in words, the questions actually evaluate specific 

relative-state characteristics. That is, the questions involve such quantities 

as positions, angles, velocities, and distances. The reader should note in 

studying these questions that the airplane controlled by the canputer model is 

referred to as the attacker. A detailed explanation of each question will not 

be given here. Two questions will be discussed, however, as typical of the 

information being evaluated. Question 1 asks whether the opponent is in front 

of the attacker. Stated in geometric terms, the question asks whether the angle 

between the attacker's X body axis and the line of sight to the opponent's 

center of gravity is less than 90°. A slightly more complex example is question 5, which asks whether the attacker is within a certain cone behind the 

7 
opponent. Specifically, the program determines whether the attacker is in a 60° 

cone behind the opponent at a range of less than 914.4 m. All 14 questions are 

evaluated for each trial maneuver to obtain the numerical score. 

The idea of flying in maneuver planes is more a goal with the AML than it 

is a fact. It provides an especially good mechanism through which to set up 

trial maneuvers and to evaluate them. Because of the mathematical scheme used 

to simulate airplane motion in the performance model, the performance model 

probably comes closer to achieving piecewise planar flight than does the control 

model. The transitioning between maneuver planes may require a large part of 

the time between decisions. During this time, the airplane's flight path is 

obviously not planar. In addition, it is often not possible to maintain planar 

flight once it has begun. The control model uses the maneuver-plane concept 

only at maneuver-decision intervals, and then only to specify a bank angle and 

a percentage of available load factor to use until the decision time occurs 

again. 

Ground Avoidance and Energy Management 

In addition to the basic concepts and guidance principles already discussed, there are two other major problems that the maneuvering logic must 

solve. One of these, as previously mentioned, is ground avoidance. The program 

continmusly canpares the flight-path angle of the canputer-controlled airplane 

with tabulated values of the maximum dive angles from which the airplane can 

recover without crashing. As the flight-path angle approaches an unrecoverable 

value, the trial maneuvers that are permitted become more and more limited until 

only a maximum load-factor pull-up in the vertical plane remains. Considerable 

care must be taken in structuring the logic to prevent crashes because groundavoidance maneuvers generally are not the most effective to use against the 

opposing airplane. 

Energy management is a second area of concern. The particular reference 

here is not to optimizing energy management but, more specifically, to preventing an airplane from flying so slowly that it can no longer maneuver effectively. The program continuously monitors the load factor available to the 

canputer-controlled airplane. When it diminishes to certain predetermined 

levels, the normal trial maneuvers are modified or different ones are brought 

into consideration. At some levels of available load factor, the airplane is 

restricted to maneuvers which do not exceed certain percentages of the airplane 

sustained load-factor capability. Under the worst of circumstances, the airplane may be required to relax load factor entirely. As with the groundavoidance maneuvers, considerable care must be taken in devising the criteria 

for the use of energy-management maneuvers. Indiscriminate use of either type 

of these special maneuvers could seriously reduce interaction with the opponent. 

The logic gives priority to ground avoidance, energy management, and normal 

maneuvers, in that order. When special maneuvers are used, generally more than 

one is evaluated by using the same selection process as is used with the normal 

maneuvers. Thus, the program at least tends to choose maneuvers which keep the 

airplane turning in the direction of the opponent. 

8 
Although the program specifies a throttle setting to be used for each trial 

maneuver, whether it be standard, ground avoidance, or energy management, situations occur during the execution of maneuvers in which the throttle setting 

should be changed. Three basic situations are covered. First, a reduction in 

throttle setting may be required to prevent the computer-controlled airplane 

froan overshooting its opponent. Speed brakes may also be employed under these 

circumstances. Second, a similar throttle change may be used to induce an overshoot by the opponent. Finally, the throttle is manipulated to cause the airplane to fly at or near its corner velocity (velocity at which the maximm lift 

capability of an airplane equals its structural load limit). 

Lag and Lead 

To simplify the earlier explanation of the AMI, evaluation of the predicted, 

relative future states of its own airplane and that of its opponent, a nominal 

value of 2 sec was specified for the prediction and extrapolation intervals. 

These values are seldom actually used, however, An important part of ACM 

strategy involves the employment of lags and leads. If the future state of the 

opponent is predicted over the same time interval that the future state of the 

AML-controlled airplane is extrapolated, the situation is near pure pursuit. 

Actually, it is a slight lead since collision-course trajectories are being 

canpared. 

If the AML extrapolates its airplane state over a longer time interval than 

it predicts its opponent airplane state, effectively it evaluates lag. Conversely, if the relative lengths of these time intervals are reversed, lead 

maneuvers are evaluated. 

For most situations, the AML uses lag rather than higher risk lead maneuvering. Unless irrevocably committed to a given maneuver, an opponent who 

recognizes that his adversary is using lead pursuit may be able to readily alter 

his trajectory to put the adversary in a disasterous situation. Only in the 

case of equal (2-sec) extrapolation and prediction intervals does the AMI, even 

approach the application of lead. The degree of lag used varies with the degree 

of positional advantage enjoyed by the AML and the range between the airplanes. 

Large lags, for instance, are employed at very close ranges to prevent 

overshoots. 

Finally, the AMI, uses long-range as well as short-range prediction and 

extrapolation. The longer the time interval over which the opponent's state 

is predicted, the less accurate it is likely to be because the opponent has 

more time to alter his trajectory. However, at long ranges where errors are 

less critical and softer maneuvering is more likely, longer prediction intervals 

may be safely used to devise longer term, less energy consumptive maneuvers. 

Even with the longer time-interval predictions, the AML still uses lag-type 

maneuvers. Long-term prediction (6 sec) is always used for ranges greater than 

1524 m and is also used for ranges greater than 762 m if the AML has already 

acquired a large angular advantage. 

9 
..... 

.. . .... ...., 

Operational Modes 

Two basic operational. modes of the AMI, program exist, the off-line or 

batch-processing mode and the real-time simulation mode. The batch-processing 

mode controls two airplanes and supplies maneuvering logic, relative geometry 

computations, equations of motion, and aerodynamic characteristics of the two 

airplanes. Since the same maneuvering logic is used for both airplanes, measures of the relative maneuvering capability of two dissimilar airplanes can 

be obtained. The program can also used to obtain preliminary indications 

of the outcome of pilot-versus-pilot studies. 

The real-time simulation mode utilizes the DMS to allow the canputercontrolled airplane to fly interactively one-on-one air-combat engagements 

against human pilots. Since the program is deterministic, it can act as an 

invariant or standard opponent whose logic and maneuvering performance can be 

kept constant from one simulator run to another, thus allowing variations in 

human-pilot performance to be measured with respect to it. The AML, in conjunction with a manned simulator, can offer significant potential in pilot 

training and proficiency maintenance. 

A more canprehensive explanation of the AML program, including sane 

detailed explanations of specific subroutines of the computer program, can be 

found in references 1 and 2. 

DESCRIPTION OF TESTS 

Objectives 

A general procedure was developed to acquire data for evaluation of the 

two AML models. Three basic sets of data would be taken. The first set would 

be recorded from runs in which pilots flew only against other pilots in the 

Dm, and would provide a baseline with which to compare the remaining data. 

The other two data sets would consist of runs in which the same pilots flew 

against each of the two AML models. Prom analyses and evaluation of these data, 

an assessment would be made of the skill and effectiveness of the AML models 

relative to those fighter pilots. In addition, the maneuvering effectiveness 

of the two models relative to each other would be evaluated. 

Procedure 

A simulated F-4 was flown by both the pilots and the AML. The F-4 was 

chosen because it had been used as the baseline airplane throughout most of the 

development work on the AML and hence was the one most effectively driven by 

the AML. Also, it was the only airplane for which a control system had been 

designed to permit control through its full equations of motion. Furthermore, 

to eliminate the characteristics of the airplane itself as a variable in data 

comparisons, it was necessary to make all runs with the same airplane. 

All runs were made in the DMS. Twelve sessions (1 session per day) of 

3 hr each were conducted over a period of slightly more than 2 weeks. Pilot10 
versus-pilot runs utilized both DMS spheres while pilot-versus-AML runs utilized 

only one. Each data set consisted of 24 runs. 

Run schedules were structured to minimize the effects of extraneous variables such as the order in which a given pilot made his runs. Each pilot flew 

four runs against every other pilot during the pilot-versus-pilot data set. 

These runs were ordered such that each pilot flew an equal number of runs in 

each of the two DMS spheres. In each of the AML-versus-pilot data sets, each 

pilot flew against the AML six times. Once a data set began, a log was maintained to show the length of each run, the identification of the pilots, the 

cause of run termination, and other such pertinent information. 

From previous experience with the Dm, it was expected that after about 

three practice sessions, the pilots would be comfortable in the simulator, and 

learning processes with respect to it would have reached a plateau. This was 

expected to be especially true by the time data sets against the AML were begun. 

Thus, no practice runs against the mr, were scheduled, and data runs were begun 

immediately upon introducing pilots to the AML models. 

The same initial conditions were used for all runs. The airplanes were 

started head-on from a range of 3.6565 km. The initial altitude of both was 

4.572 km and both had initial speeds of M = 0.9. Each data run was scheduled 

to last 3 min. 

Sixty-eight variables were recorded every 0.5 sec during data runs. These 

included state variables of both airplanes (Le., a, 8, u, v, w, etc.), 

as well as relative geometric relationships between them. 

The first 2-1/2 days of the experiment were used for familiarizing the 

pilots with the simulator. The schedule during this period was not rigid, but 

an attempt was made to approximately equalize the simulator time for each pilot. 

On the third day, pilot-versus-pilot data runs were begun. Departures and 

crashes were a problem frm the beginning. 

The extreme competitiveness of fighter pilots and the lack of lifethreatening consequences in the simulator caused a large number of runs to terminate before 3 min had elapsed. Abut 4-1/2 days were required to complete 

this data set. In fact, in order to cmplete the experiment within the constraints of alloted simulator time and available pilot time, 9 runs with durations of less than 3 min but more than 2 min were accepted as data runs. 

About 3 days were spent attempting to acquire the 6, 3-min runs for each 

pilot flying against the AML control system. Again, time constraints forced 

the acceptance of several runs of less than 3 min. 

The AML performance model was the last in the series to be run. Only about 

2 days were required to complete this set, Thus enough time was left to fill 

out the control-model data set with 3-min runs. Only the pilot-versus-pilot 

runs were left compromised by an incomplete set of 3-min runs. Upon completion 

of all runs, pilots were debriefed and their comments taped. 

11 
PII#rS 

Pilot background was important because the AML maneuvering-performance 

results were to be compared with those of typical U.S. military fighter pilots. 

The background and experience level of the pilots covered a broad range. All 

four were currently flying F-4's, which was a requisite of the experiment. TWO 

were U.S. Air Force pilots. The other two were U.S. Navy pilots. Two were 

instructor pilots. Flight experience varied from about 4000 hr in many types 

of aircraft, including helicopters, to only about 300 hr primarily in the F-4. 

The maximum F-4 experience was about 1800 hr. 

Pilots were given a fairly canprehensive briefing, including the operation 

of the DMS and AML, the purpose of the study, methods of data analysis, and 

general procedures to be followed throughout the experiment. For instance, 

they were told to request rest periods as needed regardless of the run schedule. 

There were asked to make at least one nondata run each time upon entering the 

simulator to allow their eyes to accommodate to the simulator lighting. They 

were also instructed to tell the canputer operator when they were ready to begin 

data runs. 

No particular maneuvers were specified for the pilots to use. They were 

left entirely to their own resources to devise and use the most effective maneuvers. However, they were told that no simulated weapons would be fired and that 

runs would not be terminated when a pilot acquired the necessary conditions to 

fire a particular weapon at the other. Rather, their objective was to acquire 

and retain the best possible positional advantage relative to their opponent. 

Within this context, then, certain entries into weapon zones might be examined; 

however, these would function only to give a measure of the degree of positional 

advantage. 

IiESULTS AND DISCUSSION 

Several methods of analysis and evaluation were applied to the data. Generally, these were comparisons of computed parameters which quantify various 

types and degrees of positional advantage attained by the pilots or the AMLcontrolled airplane during a run. Some statistical tests on these parameters 

were also made but were of very limited scope and depth. It is important that 

the reader not attach undue significance to these statistical tests, however. 

No absolute performance indexes have been found which measure the outcome of 

one-on-one simulated ACM. The parameters used in this paper should be considered merely as indicators of relative performance. Maneuvering in air canbat 

is too complex and includes too many variables to be measured well by a single 

index. Thus, a canprehensive statistical analysis of a particular indicator 

could possibly produce erroneous results. However, it is believed that by considering all of the analyses which will be presented here, a valid assessment 

of the results can be obtained. 

To permit direct canparisons with other data runs, some data were adjusted 

from the nine pilot-versus-pilot runs that lasted less than 3 min. The 

adjusted parameters are simply the accumulated times during a run that the 

pilot was able to remain within certain geometric zones defined by relative 

12 
headings, range, and the like. They were adjusted by multiplying the observed 

parameters by the inverse o€ the actual run length to 1 80 sec. For example, 

assune that a run lasts 120 sec and that pilot X accunulates 30 sec of parameters Y. For canparison purposes, the effective accumulated seconds of parameter Y is 

- ." 

120 

x 30 = 45 sec 

Before discussing individual performance indexes, time histories of basic 

run data will be presented. 

Time Histories 

From data recorded on magnetic tape, time histories of several airplane 

state variables, as well as computed geanetric relationships between the airplanes, were plotted. Pour of these variables that were found to be most 

descriptive of the Am engagements are shown plotted for each run in figures 7 

to 78. Of f-boresight angle x is the angle between the X body axis of a 

canbatant's airplane and the line of sight to his opponent. If this angle is 

zero, the canbatant is pointing the nose of his airplane directly at the center 

of gravity of his opponent's airplane. As x increases, the combatant points 

farther and farther away fran his opponent. The ideal situation for a given 

pilot, for example, is to have his x = Oo while that of his opponent is 180°. 

The other three variables plotted are Mach nunber, altitude, and range. Figures 7 to 30 are pilot-versus-pilot runs. Figures 31 to 54 are pilot-versusAML-control-model runs; and figures 55 to 78 are pilot-versus--performancemodel runs. These figures Frovide a broader perspective of the engagements than 

do certain signal-variable scoring criteria to be discussed later. They allow 

the viewer to follow each engagement from beginning to end. He can determine 

when each canbatant had an angular advantage as well as other concurrent aspects 

of the fight which can influence the meaningfulness of the advantage. Also, 

certain tactical maneuvers may be observed. For instance, attempts by one pilot 

to ascrape" the other off by diving toward the ground may be detected in the 

altitude plots. Likewise, overshoot situations may be detected by observing 

the Mach plots in conjunction with the range plots. 

Several general observations may be made by canparing the data in the various groups (pilot-versus-pilot, pilot-versus-AML-control-model, pilot-versusA"performancemode1). Pilots appear to sustain higher altitudes and speeds 

against other pilots than they do against the AML models. The lower altitudes 

observed in the pilot-versus-- r uns were often critical for maneuvering. That 

is, many otherwise optional maneuvers were no longer available because of the 

law altitude. The longest endured critically law altitudes are mre noticeable 

in the pilot-versus-AML-performance-model group. 

Caparisons of the X plots of the two AML models indicate that the control model was generally able to achieve a more desirable angular relationship 

against the human pilots. There are also indications that the control model 

13 
has superior low-altitude maneuvering capability. These data have been presented at this point in the discussion of results to provide the raw results 

upon which many of the analyses to follow are derived. 

Time on Offense With Advantage 

The most often used parameter to evaluate the results of engagements in the 

DS is time on offense with advantage (TOA) . It is simply the accumulated time 

that one airplane is able to keep its opponent in front of its wing line and 

simultaneously to remain behind the opponent's wing line. Although this parameter covers a wide range of conditions from close tracking to near parallel 

flight at all ranges, it still seems to be a very good indicator of relative 

maneuvering performance. Many DMS studies attest to this fact. 

Figure 79 is a bar chart showing the average TOA acquired by each pilot 

when flying against the other three pilots. The individual-run data are presented in table I. 

Figure 80 is a bar chart comparing the individual TOA scores of each pilot 

with those of the performance model against him. The mean TOA of all pilots 

is also shown for comparison with the mean TOA of AML. Figure 81 presents the 

same information for the AML control model. 

Kolmogorov-Smirnov tests for normality were applied to the data of figures 80 and 81. Fran these, it was concluded that the data could be assumed 

normal, permitting the use of t-tests to determine whether there are significant 

differences between the means of certain data groups. Overall pilot scores as 

well as those of individual pilots were compared with corresponding scores of 

the AML models. The results of these canparisons, including the individual TOA 

soores from which they were derived, are summarized in table 11. 

The following observations are noted: 

1 . For pilot-versus-AML-perf ormance-model runs - 

The mean pilot TOA is significantly less than the mean TOA of the 

AMI; (dotted lines of fig. 80). 

The individual mean TOA of pilots C and E are significantly less 

than the corresponding ones of the AML. 

2. For pilot-versus-A"contro1-model runs - 

The mean pilot TOA is significantly less than the mean TOA of the 

AML (dotted lines of fig. 81). 

The individual mean TOA of all pilots except pilot E are significantly less than the corresponding ones for the AML. 

3. The mean TOA of the pilots is approximately the same against both AML 

models. The mean TOA of the control model is much greater than that of the 

14 
performance model and differs significantly from it (student's t) at the 

95-percent confidence level. 

4. In only one comparison did a pilot TOA exceed that of the AML against 

him. Pilot D was able to accanplish this against the performance model, but the 

confidence level of the significance of the score difference is only 60 percent. 

As previously stated, clear-cut win-loss criteria for simulated A(M engagements simply do not exist. Even so, it was decided that some such evaluation 

on a run-by-run basis might provide an enlightening alternative view of the 

data. Thus, a set of criteria believed reasonable, although arbitrary, was 

defined in terms of TOA scores to permit this evaluation. To win a run, a combatant had to have acquired at least 20 sec of TOA and at least twice as much 

TOA as his opponent. All other runs were considered draws. Using these criteria, it was determined that the control model won 17 runs, tied 5 and lost 2; 

and the performance model won 12 runs, tied 8, and lost 4. 

AML SCORE 

Another parameter often used to analyze DMS results is called adaptivemaneuvering-logic score (AMLS). The AMLS is computed simply by answering the 

following questions just as the AML does to make maneuvering decisions. The 

Answers 

1. Is opponent in front of attacker? 

2. Is attacker behind opponent? 

3. Can attacker see opponent? 

4. Is opponent unable to see attacker? 

5. Is attacker in certain cone behind opponent? 

6. Is opponent not within certain cone behind attacker? 

7. Can attacker fire at opponent? 

8. Can opponent not fire at attacker? 

9. Is attacker closing on opponent? 

10. Is attacker's line-of-sight angle less than 60°? 

11. Is attacker's line-of-sight angle decreasing? 

MAXIMUM AND MINIMUM SCORES 

Yes 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

11 

- 

No 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

- 

15 
I 

questions are evaluated every 0.5 sec of each run and averaged for each adversary. The value of the AMLS parameter then ranges fran 0 to 11. Note that 

this set of questions is smaller than the set on page 7 because it represents 

an earlier version of the AML maneuver-selection criteria that was current at 

the time the DMS postanalysis programs were written. The parameter generally 

varies through a much narrower range than its possible extremes. Typically, 

its values are in the range of 4 to 6. Thus, very small changes can result 

fran very large changes in maneuvering performance. An important advantage 

that the AMLS enjoys over TOA is that it accounts for many more aspects of the 

maneuver process than does TOA. On the other hand, the user has greater difficulty visualizing what is being measured as well as deciding what level of 

incremental change in the index is significant. Experience has shown that differences of 0.5 or greater generally occur in runs having large performance 

differences between opponents. 

Figure 82 presents results for pilots flying against each other. It corresponds to the TOA data of figure' 79. Data for individual runs are presented 

in table I. 

Figures 83 and 84 are bar charts presenting individual comparisons of pilot 

AMLS values with those of the performance model and control model, respectively. 

These correspond to figures 80 and 81, respectively, which show TOA scores. 

As with the TOA data, the AMLS data were tested using Kolmogorov-Smirnov 

tests. The student's t was used to test both overall and individual pilot 

differences between pilot-AMLS means and those of the AML. Table I11 contains 

these results. 

The following observations are cited: 

1. Both AML models achieved significantly greater overall mean AML scores 

than did the pilots against them. 

2. The mean differences between the AML scores of the pilots and those 

of the opposing AML model were computed for the data of figures 83 and 84. The 

mean difference for the control-model data was found to be greater than that for 

the performance-model data, the difference being significant at the 85-percent 

confidence level using the t-test. 

3. Only pilot E achieved a higher score than AML against him in the 

control-model runs. This difference was found to be significant at only a 

55-percent confidence level, however. 

4. Only pilot D achieved a higher score than the AML against him for the 

performance-model runs. Likewise, this difference was significant only at a 

60-percent confidence level. 

Missile and Gun Zones 

In order to obtain additional insight into 

ing advantage achieved by one DMS opponent over 

the degree of relative maneuveranother, simulator runs are 

, 

I 

16 
often analyzed to determine the time a pilot entered a defined weapon zone and 

how long he remained there. The zones used seldan bear more than a token relationship to any existing weapon, as is the case for all zone analysis performed 

in this paper. For this study, three zones were examined. A pilot was considered to have satisfied the parameters for entry into missile A zone if he 

reduced his off-boresight angle to less than 40° and maintained his range 

between 0.2096 km and 5.4864 km. Likewise, he entered missile B zone when his 

off-boresight angle was less than 20° and the range was between 0.609612 km and 

3.6576 &. To enter the gun zone, a pilot's of f-boresight angle had to be less 

than loo while that of his opponent was greater than 120° and the range less 

than 0.91 21 8 km. As can be seen, the difficulty of achieving these zones 

increases greatly from A to B to the gun zone. 

Table IV contains the time of first entry and the duration of retention 

of each of these zones for each data run, as well as averages of these times. 

Figures 85 to 96 present most of the data of table IV in bar-graph form. In 

figures 85 to 88, the common opponent referred to is a control group consisting of the four human pilots. The control group was reduced to three pilots to 

obtain data for an individual human pilot since he obviously did not fly against 

himself. Thus, the common opponent for AML was four pilots. The common opponent for each pilot was the three other pilots. The reader is cautioned, in 

examining this analysis, that acquiring any of these zones may not necessarily 

have been the pilot's objective. Pilots were not instructed to seek these zones 

nor are the AML goals tailored specifically toward them. In fact, maneuvering 

to acquire the zones in the short term may be counterproductive to attaining 

a more secure long-term advantage. It is believed, in general, however, that 

rapid acquisition of these zones is consistent with getting and keeping the 

greatest positional advantage for the most time. 

Some general characteristics and trends may be observed in the zone data 

of table IV. Generalizations will be grouped to assess: (1) how well pilots 

did against each other and against the AML models, (2) how well the AML models 

did against the pilots, and (3) how well the AML models did relative to one 

another. 

Pilots: 

1. Pilots were able to first enter missile A zone against each other and 

against the control model in about the same amount of time. 

2. Pilots were able to acquire missile B zone against both AMI, models much 

sooner than they were against each other. 

3. Pilots were able to remain in missile A zone against both of the AML 

models for approximately the same amount of time. Likewise, they were able to 

remain in missile B zone against both models for another duration of time 

approximately equal for both models. But they were able to remain in these 

same zones somewhat longer against other pilots. 

4. Pilots were able to enter the gun -ne against each other in 58.33 percent of the runs. They made entries against the control model in only 12.5 per17 
I I I I 

cent of the runs and against the performance model in only 4.2 percent of 

the runs. 

AML models: 

1. Both AML models were able to acquire both missile zones much sooner 

than pilots were able to acquire these same zones against each other or against 

either AML model. 

2. Both AML models were able to remain in both missile zones much longer 

than pilots were able to remain in these same zones against each other or 

against either AML model. 

3. The control model made entries into the gun zone in 12.5 percent of 

the runs. 

4. The performance model made gun-zone entries in 8.34 percent of the runs. 

AML models relative to one another: 

1. The control model enters missile A zone slightly sooner against pilots 

than the performance model does and remains in both missile zones longer. 

2. The performance model enters missile B zone against pilots much sooner 

than the control model does. 

3. Pilots enter both missile zones much sooner against the control model 

than they do against the performance model. 

4. Pilots remain in both missile zones somewhat longer against the perfoil-"" 

mance model than against the control model. 

5. Bearing in mind that there were very few gun-zone entries by either 

pilots or AML against each other, out of the number of runs made, pilots made 

three times more entries against the control model than they made against the 

performance model, and the control model made three to two more entries against 

pilots than the performance model did. 

Assessment of Results 

This study has addressed two primary questions: 

1 . How well does the AML perform against current US. fighter pilots? 

2. How do the two types of AEaL models canpare with one another? 

The performance of the AML mdels against human pilots was impressive, 

indeed. This was especially true of the control model. The same methods 

generally used to analyze the results of DMS studies were applied to the data 

fran this study. In each analysis, the result was the same. The AML models 

18 
The only real difficulty in addressing the question of how well the AML 

models did against the pilots was determining the amount by which the AML models 

won. The usual methods of analyzing DMS data are generally considered more 

applicable to determining trends in results than to determining the absolute 

magnitudes of relative performance. Thus, this report simply concludes that 

the performance of the AML models was far superior to that of the pilots. 

In all fairness to these pilots, however, the AML did not win all of the 

runs. Furthermore, there was a great deal of variation in pilot performance. 

Not only were sane individual runs won by pilots, but there were even cases in 

which an individual pilot either on the average won his runs against a given 

AML model or came very close. For instance, figures 80 and 83 show that pilot D 

defeated the performance model both on the basis of TOA and AMLS. However, figures 81 and 84 show that pilot D did very poorly against the control model with 

the same measures. Likewise, pilot E was only slightly inferior to the control 

model on the basis of TOA and was superior on the basis of AMLS. Conversely, 

he was substantially inferior to the performance model on both bases. Zone data 

(table IV) show similar trends for both these cases. 

Before discussing the relative performances of the two AML models, perhaps 

a better understanding of the data analysis in general can be achieved by considering the apparent fact that the character of engagements between human 

pilots and AML models is different fran that between human pilots only. 

Against the AML models, pilots produced measures of performance that were less 

variable than those they produced against each other. They much less frequently gained positions of extreme advantage against the AML models than they 

did against each other. But it is also true that the AML models seldom achieved 

the most desirable positions of relative advantage against the pilots. (See gunzone data.) Looking at the question matrix on page 7, it can be seen that the 

AML gives about equal weight to defense and offense. Note that for most questions relating to an offensive advantage, there is a corresponding question 

relating to whether the opponent will gain that same offensive advantage. The 

structure of this question matrix, then, causes the AML to tend to choose maneuvers that are conservative. It will risk giving up one aspect of positional 

advantage only with the reasonable assurance of gaining at least as much offsetting advantage. Within the spectrum of maneuvers available to the AML, the 

option to run away does not exist. This is an aggressive constraint. Except 

for this constraint, then, the AML behavior seems to have defensive leanings. 

The human pilot is much less constrained. He can do long-range, as well 

as short-range, planning. He can remember weaknesses of various opponents and 

structure tactics fran the very beginning of a fight to capitalize upon them. 

He can also use such tactics as hit and run. On the other hand, pilots are 

sanetimes at a disadvantage because of an element of inflexibility that results 

from rigid tactical philosophies. The possible disadvantages of a particular 

tactical philosophy may not readily show up as long as both opponents employ 

it and thereby neutralize the effects of its weaknesses. Along with the pilot's 

19 
larger array of optional maneuvers go more ways to make disastrous mistakes 

and to capitalize on those of others. Thus, often pilots maneuver to the most 

extreme positions of relative advantage against each other. On the other hand, 

against the AML, the human pilot's maneuver choices becane more limited because 

of the necessity to counter the AML narrower range of maneuvers. Being a deterministic tactical philosophy, the AML makes no mistakes although the maneuver 

it chooses may not necessarily be effective. The point is the AML is consistent, does not give up, continuously implements sound grounbavoidance and antideparture measures, and uses reasonably good one-on-one air-combat maneuvers. 

An opponent possessing those characteristics has a tremendous psychological 

advantage in that it does not afford the opposing pilot many mistakes. 

Even when a pilot is able to achieve a fairly good positional advantage 

over the AML, it is difficult for him to carry through to a good tracking solution because the AML models are extremely difficult to track and easy to overshoot. Likewise, perhaps because of their conservative maneuvering, the AMt 

models also seem to have difficulty maintaining extremes of positional advantage against pilots. 

Formulating a fair assessment Of the relative maneuvering effectiveness of 

the two AML models against human pilots is difficult. Part of the assessment 

can, of aourse, be done in a straightforward manner since both models were 0-rated against human pilots as a common opponent. Examinations of figures 79 

to 96 and the question set on page 15 reveal that, in an overall sense, all 

three primary measures of performance ("(X, AMLS, and zone analyses) indicate 

that the control model outmaneuvered the pilots by a much greater difference 

than the performance model did. What is not known is how much of this result 

was produced by the order in which these models were flown against the pilots. 

However, the fact that the control model was pesented to the pilots first and 

also defeated them by a much greater margin has positive implications. Even if 

the learning effects were large, the relative performance adjusted for learning 

effects would still probably show the control model to be at least as good as 

the performance model. As long as the control model performs at least as well 

as the performance model, it seems reasonable to assume that the performance 

model has not been "cheating." Any maneuvers that the performance model may 

have used against pilots which the pilots could not also use would not have made 

the performance model win by any greater amount than the " uncheating" control 

model would have won by anyway. 

Sane evidence exists that pilot learning contributed to the performance 

difference between the two models. Since both AML models are driven by identical maneuver logic, their performance against the same opponent should differ 

only to the extent that the ability of the two models to implement control 

commands differs. It seems reasonable to expect the performance model to 

execute control commands faster and more precisely, especially under low-speed 

conditions, because its attitude-control authority is constant. Its modeling 

scheme does not account for the influence of dynamic pressure on control authority. Only at high speeds should the control model have superior attitude control. Of course, one would expect this to produce a result opposite fran that 

obtained . 

20 
The fact that the mean pilot TQA was about the same for both models but 

that the mean AML TaA was much lower for the performance model than for the 

control model suggests that the pilot's defensive tactics imgxoved by the time 

the performancemodel runs were made. 

A comparison of AMLS parameters shows that the pilots' mean score improved 

fran the control-model runs to the performance-model runs while the mean score 

of the AML models worsened. That both scores changed is to be expected and is 

oonsistent with TQA results since AMLS parameters are canposite measures of 

both offense and defense. Although the AMLS parameters indicate that the performance difference between the models is large, it yields little, if any, 

information about the contribution of learning effects to the difference, 

In an attenpt to gain further insight into what caused the AML scores to 

change as they did, the AMLS paramete1 was slightly modified and recomputed. 

The objective was to divide the total score into offensive and defensive canponents to see how the constituents changed. To do this, question 9 of the question set on page 15 was omitted because it did not seem to reasonably fit into 

either an offensive or defensive classification. The remaining 10 questions 

were divided into 5 defensive and 5 offensive ones. Offensive and defensive AML 

scores were computed and tabulated (table V) , along with a recomputed A"J based 

on the 1 0 questions. 

Offensive: 

1 , Can attacker fire at opponent? 

2. Is opponent in front of attacker? 

3. Is attacker behind opponent? i 

4. Is attacker in certain cone behind opponent? 

5. Is attacker's line-of-sight angle less than 60%' 

Defensive: 

1. Can opponent not fire at attacker? 

2. Can attacker see opponent? 

3. IS opponent unable to see attacker? 

4. Is attacker's line-of-sight angle zero or decreasing? 

5. Is opponent not within certain cone behind attacker? 

It appears that the defensive AMLS generally varies about sane naninal 

value by only small increments although these variations may be meaningful. 

The offensive AMLS, on the other hand, undergoes much larger variations. 

21 
These results show that the pilots were able to improve their offensive 

AMIS against the performance model relative to the control model by about 

8.4 percent while causing the AML offensive score to decrease by 10.7 percent. 

The increase in pilot offensive score along with the decrease in the AML offensive score fran the control-model runs to the performance-model runs reinforces 

the idea that the primary difference between outcomes of the runs against the 

two models was caused by the pilots' learning how to neutralize sane of the AML 

offense. The increase in the pilot's offensive score may be accounted for by 

the fact that, in sane runs, the pilots were able to defend themselves well 

enough to allow implementation of sane successful offensive maneuvering. 

Although every way that the data have been analyzed shows the control model 

to have done at least somewhat better than the performance model, it is felt 

that the data available from this study are insufficient to conclude that either 

model possesses inherently superior maneuvering performance with respect to the 

other. 

If, in fact, neither model has any real maneuvering advantage over the 

other, a very good situation exists. Much simpler airplane models requiring 

only performance data may be quickly assembled for use in air-canbat simulation 

studies or pilot training. Control models will, however, continue to benefit 

fran enhanced pilot confidence and a sense of fair treatment. Care should be 

exercised in assuming that the results of this study apply to any other simw 

lated airplanes. The relative maneuvering performance of the two different 

models may be very different for airplanes other than the F-4. 

PILd OMMENTS 

During the course of this study and immediately follawing its completion,: 

several comments were extracted from discussions in which the pilots gave their 

opinions of the AML programs. The pilots' opinions were directed toward the 

overall operation of the real-time programs, the individual characteristics of 

each program, and how the programs compare with each other. 

The pilots were in mutual agreement in their opinions on the overall operation of the AML programs. They felt that the AML had tremendous potential as 

a training aide. They felt it could increase the proficiency of the pilots 

since it was always aggressive and appeared to do the right things. However, 

the pilots were of the opinion that the AML did not always utilize the full 

capability of the controlled airplane at low altitude. In addition, they all 

believed that the AML did not adequately take advantage of opportunities to 

improve its angular advantage in many situations. It is believed these weaknesses can be improved with refined low-altitude tactics and with more accurate 

predictions of the relative state between the controlled airplane and its 

opponent. 

In discussing the individual characteristics of each program, the pilots 

noted that the control-model program was more likely to work the vertical 

whereas the perf ormance-model program stayed low and tended to minimize its 

vertical maneuvering. They felt that the control-model program performed more 

rolling-type maneuvers than the performance model. One pilot thought that the 

22 
performance model was unable to perform many of its selected roll maneuvers 

because of its low altitude. A canplaint was registered against the 

perf ormance-model program. Two of the pilots felt that the performance model 

occasionally made unrealistic changes in attitude. This could possibly be 

caused by the incomplete modeling of the airplane's body rates. The other two 

pilots did not express their opinions on this subject; however, all the pilots 

agreed that the control model was smoother than the performance model in attitude changes. 

The pilots had varying opinions as to which of the two models was the 

better opponent. One pilot said it was difficult for him to say. Two of the 

pilots, however, made more definite statements on the subject. One said that 

the performance model was better because it was harder to predict and appeared 

to have better control of the fight. The other pilot felt that the control 

model was easier to predict and would, therefore, allow him to enjoy more success in combating it. According to the opinion of this last pilot, one could 

deduce that the overall performances of the two models were about equal. He 

stated that the control model was more difficult to stay behind than the performance model; however, it was more difficult to get behind the performance 

model. 

FUTURE APPLICATIONS 

It is recommended that several areas of research be pursued with the AML. 

In order to continue to improve the tactical logic, the capability of riding 

in the AML-controlled airplane needs to be implemented. All that is required 

is to drive the display systems in the second DIG sphere. This simple task 

would permit fighter pilots to observe the functioning of the AML from two 

points of view, thus supplying them with a much broader insight into the 

strengths and weaknesses of the logic. This information could be supplied to 

engineers who would use it to improve the AML. 

Through advanced displays, the AML could provide suggested maneuvers to 

fighter pilots. The AML could even assume control of a fighter airplane in 

emergencies such as complete loss of pilot consciousness during sustained 

high g maneuvering. 

The AML might be used to guide remotely piloted vehicles that act as wingmen for fighter airplanes. Studies need to be done to assess the most effective 

ways of employing vehicles of this type and to determine how the human pilot 

would coordinate and comnunicate with such partners. 

The principles of the AML need to be extended to the multiaircraft environment. Here one might envision a two-tier logical process. Rather than evaluate 

maneuvers against only one airplane, it would evaluate a set of maneuvers for 

each of n hostile airplanes, choosing the most effective to be used against 

each. These n chosen maneuvers would then be reevaluated on an overall level, 

selecting the one which exposes the AML airplane to the least threat fran n 

hostile airplanes and which, in addition, provides the greatest opportunity for 

aggressive activity. Furthermore, if more than one AML partner is present, the 

23 
... ,.".._ ."_." .. 

maneuvers could be assessed on an even higher (third) level, allowing one AMLcontrolled airplane to cane to the aid of another. 

CONCLUDING KEMARKS 

A digital-canputer program known as adaptive maneuvering logic (AML) has 

evolved from a batch-processing program driving greatly simplified airplane 

math models in interactive air-canbat maneuvers against each other to a highly 

refined real-time program driving a complex math-modeled F-4 airplane in successf ul, interactive, simulated air-canbat maneuvers against skilled human 

pilots. If the pilots in this study had encountered, for the first time, realworld hostile airplanes controlled by a logic that performs even close to as 

well as the AML, they would have suffered near total defeat. Of course, many 

problems would have to be solved to implement the AML in real-world airplanes. 

However, the technology to solve these problems will soon be available if it 

is not already. Three primary technical problems must be overcome. First, 

sensors mmt be developed that are capable of determining with spherical coverage the relative position of the opponent in space. Although the present AML 

programs use perfect sensor information, they probably can perform well with 

much less accurate information. Studies need to be made to assess this requirement. Second, the airplane would need computers with sufficient capacity to 

perform the AML functions. Again, the magnitude of this requirement has not 

been determined. The requirements would be not nearly so great, however, as 

those presently to drive the Dm-AML program, which must carry out many mre 

functions than just AML computations. Third, the AML now controls its airplane 

in an idealized aerodynamic environment. There are no winds, no gusts, and the 

like, and the airplane model obeys exact equations of motion. The control system would have to be reconfigured to function in a real-world environment. 

Although not proven by this study, the results indicate that performance 

models which are much more readily programmed and which require fewer cmputations than control models may be sufficient for many applications of the AML. 

This study has also greatly enhanced confidence in the results obtained from 

earlier studies that used performance models. 

Langley Research Center 

National Aeronautics and Space Administration 

Hampton, VA 23665 

July 18, 1979 

24 
APPENDIX A 

PERFORMANCE-MODEL MODIFICATIONS 

Bobby 3. Glover 

Kentron International, Inc. 

Appendix A covers modifications made to the real-time and batch AML 

performancemodel programs. St should be noted that this appendix does not 

cover all the changes made to the original version of the AML. Instead, it primarily covers the major changes that were made after the contractor reports of 

references 1 and 2. Many other changes such as plotting capabilities, printout, 

minor logical manipulations, and program sequencing are not discussed. 

The modifications are discussed al@abetically with respect to the sub 

routine that contains them even though some are not directly applicable to the 

basic function of the subroutine. The discussions may appear brief since no 

attapt was made to cover previously published material. Therefore, it is 

necessary for the reader to have a good understanding of references 1 and 2 

before he can fully canprehend the meaning and impact of some of the changes. 

A current program listing would also be a valuable tool when reviewing the 

changes. The AML canputer programs LAR-12301 and LAR-12553 are available fran 

COSMIC, 112 Barrow Hall, University of Georgia, Athens, GA 30602. 

Each individual change made to the AML programs was verified in either the 

real-time or batch mode, and generally was verified by both. Evaluation of the 

results revealed that most changes improved the AML capability. 

Subroutine AERE'4 

Although subroutine AEW4 retains the same name used in previous versions 

of the AML gxograms, the interpolation intervals and variable output have been 

reconstructed. The subroutine now performs a linear interpolation and outputs 

the variables given in the table on the following page. 

With the exception of the thrust variables, the variables listed are either 

new or their data arrays have been expanded or modified to allw program consistency with airplane simulation programs on the Langley differential maneuvering 

simulator (DMS). The new variables are maximm and sustained lift coefficients, 

corner velocity, and angle of attack. The corner velocity is the Mach nmber 

at which the controlled airplane can achieve its highest turn rate. 

The tables of maximum and sustained lift coefficient were installed in 

AERE'4 to replace the tables of maximun and sustained load factor. The storage 

interval, along with the linear interpolation, had caused excessive drag, especially in the Mach regime between 0. and 0.5. In this regime, load-factor data 

were stored at Mach nunbers of 0.2 and 0.5. Load factor was held constant below 

M = 0.2 and computed by linear interpolation between Mach numbers of 0.2 

and 0.5. It was discovered that by holding the load factor constant at 

25 
APPENDIX A 

Var i ab1 e 

Military thrust 

Afterburner thrust 

Idle thrust 

Speed-brake drag 

Corner velocity 

Dive recovery angle 

Maximum lift coefficient 

Sustained lift coefficient 

Coefficient of drag 

Angle of attack 

M = 0.2, incorrect lift coefficients CL 

FORTRAN 

name 

TMI LX 

TABX 

TIDLEX 

CDSBX 

CoRVx 

RECANX 

TCLMX 

TCISX 

CDX 

ALFATX 

Function of - 

were computed and used in the table 

lookup for drag. The CL used in the table lookup was canputed by the following equation: 

(W) (Load factor) 

CL = 

cis 

- ~. 

Since 4 decreased with decreasing Mach nunber while load factor, w, and 

S remained constant below M = 0.2, CL increased, which caused drag to 

increase (C, increases with CL). An atttempt was made to correct this problem by computing load factors between M = 0. and M = 0.2 with the same linear interpolation scheme that is used to canpute load factors between M = 0.2 

and M 3: 0.5. However, this linear interpolation still did not provide the 

required accuracy. Ultimately, the load-factor tables were replaced with tables 

of lift coefficients. The value of CL was assumed to remain constant below 

M = 0.2. This change has proven quite satisfactory. 

The corner velocity, which was incorporated into this subroutine, is tabulated as a function of altitude and is used by the throttle-control subroutine 

in determining the throttle setting during normal maneuvering conditions. The 

throttle logic sets the throttle position to idle, military, or afterburner 

thrust in order to maintain the designated corner velocity. Consequently, the 

airplane's speed control is aimed at a level near that at which it can achieve 

its highest turn rate. 

The table lookup for Or as a function of CL and M replaced the Or 

computation, which was based on a linear relationship between CL and Or by 

26 
APPENDIX A 

with CL~ being a function of Mach and altitude. This computation, due to the 

linear assumption, caused incorrect values for CC at high CL and consequently 

created a misalinement problem in the body axes of the controlled airplane. 

The tabulated values of as a function of CL and M not only eliminate the 

linear assumptions but also provide a better definition of CC over a broader 

regime. 

Subroutine EWTT 

EQMOlT is one of the more important subroutines contained in the performance versions of the AML programs. It has the responsibility of executing the 

selected maneuvers. Force and attitude equations are driven by its controlled 

bank, load factor, and thrust designations until the desired situation is 

achieved. Digital filtering is provided each cycle for the rotational rates 

of the controlled airplane's body axes. This smooths the attitude transition 

of the controlled airplane when it is commanded to change its maneuver plane. 

Several modifications were made to this subroutine to enhance the simulated 

flight characteristics of the controlled airplane. The subroutine is now more 

effective in executing the selected maneuvers, which increases the overall performance of the maneuver logic. Because of the length of the modifications and 

the involvement of the logic, only the basic concepts of the changes and their 

contributions to the performance or realism will be discussed. 

Computations of aerodynamic quantities and roll angle.- These computations 

pertain primarily to the transition mode in which the controlled airplane rolls 

from one maneuver plane to a new commanded plane. Prior to modification, the 

magnitude of lift, angle of attack, drag, and thrust was held constant during 

transitions between maneuver planes. This reduced the validity of the model 

and created unnecessary discrepancies between the Euler angles of the maneuver 

plane and the Euler angles of the controlled airplane body axes. These discrep 

ancies prevented the maneuver logic from selecting the best maneuver for the 

airplane. In order to correct this problem, the flow of the subroutine was 

rearranged. The canputations of the previously mentioned quantities are currently performed during every iteration. 

The bank-angle canputation, which is also performed during the transition 

mode, currently updates the basic roll-angle command by the amount that the controlled airplane rolled during the previous program iteration. Previously, 

maneuver-plane transitions were performed by driving the attitude equations 

with a constant roll rate for a canputed number of program iterations. The 

number of program iterations required for a particular transition was dependent 

on the magnitude of the desired roll-angle change and on the maximum roll rate 

of the controlled airplane. This technique created maneuver-plane errors when 

the airplane was unable to achieve the commanded roll angle. The present roll 

transition process drives the attitude equations to null the error between the 

commanded roll angle and the airplane's current roll angle. 

CL filter.- Jerky and erratic motions had been noticeable in the real-time 

flight trajectories of the controlled airplane when it was required to perform 

a maneuver with a much higher or much lower CL than the previous one. It was 

27 
APPENDIX A 

determined that maneuvers sf this nature caused large instantaneous changes in 

the controlled airplane's loading, thus changing its state in an unrealistic 

manner. To correct this problem, logic for filtering the lift coefficient was 

developed and installed in EQMOTT. 

The basic concept of the filter is to increase or decrease the CL of the 

previous maneuver by small constant increments until the required CL is 

reached. This smooths the flight trajectory by eliminating the instantaneous 

load changes. 

Situation energy management.- The energy-management function slightly 

digresses from the basic function of EQMOTT. It is part of the tactics logic; 

however, due to the accessability of the required variables, the logic was 

installed in EQMOTT. 

The energy-management logic reduces the magnitude of a commanded load 

factor for certain situations that occur after a near head-on pass. The magnitude of the load-factor reduction is dependent on the type of airplane that is 

modeled. However, in most cases, the level will be between what the airplane 

can sustain and its maximum capability for a given condition. This allows the 

airplane better management of its energy and at the same time provides a positional situation which is as good as the situation achieved when the airplane 

utilized its f 1.111 load-f actor capability. 

After a near head-on pass, the commanded load factor is reduced (1) when 

the two airplanes have moderately large separations without the opposing airplane having a small x and (2) when the airplanes' separation is not large, 

but they have large separation rates and large values of x. 

"Over-thetop- .Fobla.- For some time, it was observed that the AML performance model occasionally had difficulty canpleting vertical loops. It would' 

appear to lose interaction with the opponent and roll in a confused, unordered 

fashion, often resulting in a hammerhead stall. The airplane just could not 

get "over the top. If the plane of the loop was inclined slightly from the 

vertical, however, the problem was not apparent. 

The mechanics of the over-the-top problem are involved and difficult to 

discuss with reasonable brevity. Especially with the performance model, considerable background familiarity with the way in which the equations of motion are 

modeled is required to fully understand the problem and its solution. For mre 

cmplete information, the reader is referred to pages 19 through 26 of reference 2, as well as to an actual current FORTRAN listing of subroutine EQMOTT. 

A very brief background will be presented here, but it is considered minimal, at best. Recall that the performance model utilizes a number of approximations to characterize its equations of motion. It must do this because it has 

no means of implementing commands from the logic by deflecting control surfaces. It simply assumes that the commanded bank angle and load factor can be 

achieved and essentially places the airplane in the commanded attitude. To 

maintain a degree of reality and continuity in the attitude motion, rotations 

about the body axes are driven by commanded body rates that are filtered and 

limited. As has already been discussed, the angle-of-attack change is smoothed 

28 
APPENDIX A 

indirectly by filtering changes in lift coefficient. In all computations that 

are made to drive the airplane's attitude, the angle of sideslip is considered 

zero. This does not mean that it actually is zero, only that it is not 

accounted for in determining attitude driving rates. 

Since the velocity vector is very accessible, it provides a good reference 

on which to base many canputations. Thus, an axis system called the movingmaneuver-plane (MMP) axis system is defined with the velocity vector serving 

as its X-axis. Rotations made about the velocity vector by using the "righthand rule" define maneuver planes in which the airplane is controlled to fly. 

During transitions fran one commanded bank angle to another, the airplane is 

assumed to fly in intervening "instantaneous maneuver planes," with its wings 

perpendicular to them. The transition continues until the maneuver plane corresponding to the new commanded bank is reached. Both the X-axis and the Z-axis 

of the MMP system lie in the maneuver plane. In general, the axis system of 

airplane body relates to the maneuver plane in exactly the same way. That is, 

its X- and Z-axes are also contained by the maneuver plane. Since the sideslip 

is assumed to be zero, the velocity vector and the X body axis are always sep 

arated by c1. Lift and drag forces are set up directly in the MMP with thrust 

forces transformed to it by 01. The direction cosines of the "P axis system 

are used to transform the forces to the inertial axis system. 

Concentrating on the rolling process, now follow through a cycle to see 

how the driving body rates are determined. To effect the roll maneuver, the 

airplane is assumed to roll at some fixed rate. From this, a fixed number of 

degrees of roll per iteration are canputed. This incremental change in bank 

angle then becomes the goal or desired change during the current program iteration. Since the incremental bank of the airplane will be the same as that of 

the instantaneous maneuver plane, a unit vector along the X body axis may 

readily be transformed to the MMP system. It will project along the X and 

2' W axes by the cos c1 and sin' 01, respectively. The value of c1 is 

determined from a table for the current conditions of Mach number and lift 

coefficient. The direction cosines of the "P system are used to ultimately 

determine the inertial canponents of the unit vector. These are then used to 

compute the EuPer angles ($ and 8) of the attitude to which it is desired to 

drive the body during the current program iteration. From these desired Euler 

apgleg, the +own present ones, and the iteration rate, the required Euler rates 

($, 8, and 9) are canputed. They are transformed to body rates (p, q, 

and r), filtered, and limited. Finally, the body rates are used to update 

body quaternions which yield the airplane's new actual attitude at the end of 

the current iteration. 

The scheme functions well as long as the Euler roll angles of the MMP and 

the body are approximately the same. It should be noted, however, that although 

an incremental roll about the X-axis of the MMP is exactly the same as that 

required about the X body axis of the airplane in order to maintain its wings 

perpendicular to the maneuver plane, the corresponding Euler roll angles of the 

"P and the airplane body are not equal in the general case. They may both 

legitimately be zero, or they may both legitimately be _+1 80°. Otherwise, they 

are equal only if the angle of attack is zero. The performance model, however, 

makes the assumption that they are always equal. The assumption is a good 

approximation throughout most of the flight regime. Although the accuracy of 

29 
APPENDIX A 

the approximation decreases as c1 increases, C% never exceeds 30°. Thus, by 

itself, it creates no great problems. The accuracy of the approximation also 

decreases as the magnitude of the pitch angle of the velocity vector increases. 

Severe breakdowns of the approximation take place if both the velocity vector 

and the X body axis are contained by a vertical or near vertical plane (perpendicular to the X-Y inertial plane) and the pitch angle of the velocity vector summed with the angle of attack exceeds 90°. A similar situation occurs 

in the vicinity of -goo. Consider, for example, a vertical-plane situation in 

which both the velocity vector and the X body axis have positive pitch angles, 

with the pitch angle of the X body axis being the greater. The Euler roll 

angles of both the MMP and the body axis system must be zero. If the airplane 

continues to pitch upward, it will pass through a pitch angle of +goo. When 

it does, its body-axis roll angle should change from Oo to 180° instantaneously, 

but the roll angle of the MMP obviously should remain zero until the velocity 

vector transitions through 90°. Clearly, this is the worst possible conflict 

between what should happen and the approximation of equal Euler roll angles. 

As previously mentioned in the discussion o€ this subroutine, two different control modes operate within the routine. In the maneuver mode, the objective is to sustain planar flight once the commanded maneuver plane has been 

reached. The desired roll angle of the body axis is determined from the bank 

of the MMP, which is the reference to be maintained. If the geometric conditions are such that the approximation of equal Euler angles breaks down, the 

body-axis roll is driven in a meaningless way. If the same conditions exist 

in the transition mode which drives the body from one commanded maneuver plane 

to another, an incorrect approximation is made to determine the MMP Euler roll. 

Thus, in one case, the bank of the MMP is correct, but the corresponding bank 

of the airplane is not, and vice versa. Both cases result in unrealistic 

dynamic behavior. The forces acting on the airplane are improperly oriented 

and/or the attitude of the airplane is driven in an improper manner. 

The vertical-plane example cited is a maneuver-mode case. When the body 

axis passes through +goo, the quaternions recognize this and correctly switch 

the body roll to fl 80°. The approximation of equal Euler angles, however , requires that body roll be equal to the MMP roll which is still zero. Thus, 

large body rates are imposed upon the quaternions to accanplish this. 

In an effort to reduce the problem, a "fix" was devised for the most severe 

cases. Specifically, the vertical-plane case was attacked. Two conditions 

identify the vertical-plane problem. The plane is determined to be vertical 

if a unit vector along the cross product of the X body axis and the velocity 

vector does not project onto the inertial Z-axis. Secondly, for the equal 

Euler angle approximation to break down, the body axis and the MMP axis must 

be on opposite sides of the 90° pitch-angle point. This condition exists if 

their respective Euler yaw angles differ by 180°. Inexact conditions are 

applied to the identification of the vertical-plane situation to cause planes 

inclined slightly to either side of the vertical to be identified and treated 

in the same manner as the vertical planes are treated. 

If these vertical-plane conditions are present, the Euler roll angles are 

recognized to be different and are assigned corrected values. In addition, no 

30 
APPEIX A 

changes in bank-angle commands are permitted until the airplane has maneuvered 

out of the abnormal condition. 

Although this fix has not completely eliminated the problem, it has greatly 

improved maneuvering effectiveness of the performance model. 

The previous over-the-top problem is no longer a noticeable deficiency in 

the model's dynamic behavior. On the contrary, it is very competitive with 

smooth, realistic attitude motion. Yet it is believed that further improvement 

can and should be made by those who use the program in the future. 

It should be possible to drop the equal Euler angle approximation entirely 

and replace it with expressions which give the second required Euler angle in 

terms of the known one. The attituge-of the MMP axis system may be expressed 

in terms of three Euler rotations ($,e,@) frm the inertial axis system. Fran 

the MMP system, the body-axis system may be expressed as a fourth rotation a 

about the MMP Y-axis. Multiplying the four matrices associated with each rotation (beginning with inertial axes), a three-by-three direction cosine matrix 

locating the airplane's body axes in terms of the inertial axes may be obtained. 

Individual terms in this matrix may be equated to corresponding terms in the 

standard three-by-three direction cosine matrix which relates one axis system 

to another in terms of three standard Euler rotations. Designating the three 

rotations as simply 9, 8, and $ and considering them to be Euler rotations 

from the inertial axes to the body axes, the rotation @ may be obtained by 

dividing the second element of the third column by the third element of the same 

column. The expression obtained is 

Manipulating this expression yields the corresponding expression for the Euler 

bank of the "P in terms of the Euler bank of the body. 

In the maneuver mode, the expression fog @ should be used. Likewise, 

in the transition mode, the expression for @ should be used. Some difficu_lties may be encountered with the sign of the radical in the expression for 0 

and will require additional logic. However, since it is known that for angles 

of attack equal to zero, @ must equal @, it can be shown that the positive 

radical will satisfy this condition for positive values of 0 4 90°. For - 

1800 2 @ > 900, the negative radical should be used. If @ is negative, Q 

will also be negative. Thus, @ should be limited such ghat 180° 2 (I > -1800. 

The 1@1 should be substituted into the expression for @. Finally, the 

31 
APPENDIX A 

computed 5 should be given the same sign as 4. Similar lessoning may be used 

to resolve difficulties encountered in canputing 4 fran $. Sane problems may 

also still OCCUK with the Euler angles at pitch angles of +goo, but a fix similar to that in the present program should be able to handle it. 

Subroutine EXTRT 

Subroutine EXTRT predicts the opponent's position, velocity, and attitude 

at the end of a preselected time in the future. The technique involves making 

a polynomial curve fit through the present and two past points along the opponent's flight path. These points are 1 sec apart and represent the opponent's 

position in three dimensions. Once the polynomials are obtained, the opponent's 

state variables are extrapolated to a designated time. 

It was determined by evaluating real-time and batch-processing data that 

maneuvering of the AML-controlled airplane is enhanced by making both the opponent's extrapolation time and the controlled airplane's prediction-time variables 

which are dependent on the relative states of the airplanes. In effect, depending on the values of the times, it can be assumed that this technique can be 

used to make the controlled airplane either lead, lag, or purely pursue the 

opponent. Since the lead, lag, and pure-pursuit situations have previously been 

discussed in the body of this report, they will not be discussed as such here. 

Instead, the conditions are discussed for changing the extrapolation time and 

the values obtained. It must be remembered that the controlled airplane's prediction time is varied in conjunction with some of the opponent's extrapolation 

times. The discussion of the variable prediction-time logic for the controlled 

airplane is contained in subroutine REACTT. 

The values of the opponent's extrapolation time were chosen by evaluating 

the performance of the AML while varying the extrapolation time in conjunction 

with various range and relative deviation-angle conditions. A situational tm 

logic controls the various values that can be assigned to the time. The logic 

keys on range and the deviation angles of the opposing airplane in determining 

if the extrapolation time is to be changed fran its nominal setting. Deviation 

angle is defined as the angle between the line of sight and the velocity vector 

of the airplane. Depending on the value of the previous variables, the extrapolation time is allowed to vary between a minimum of 1 sec and a maximum of 

4 sec. The 4-sec extrapolation is used for ranges greater than 1524 m and for 

situations in which the AML has achieved a fair,ly good tracking solution. The 

minimum value of 1 sec is selected when the range is below 304.8 m and the 

opponent has an angular advantage. Once the range increases and the opponent's 

angular advantage decreases, the extrapolation time is increased to 2 sec. In 

case none of these situations exist, the extrapolation time retains its nominal 

setting (1.5 sec) . 

Subroutine GETRXN 

GETRXN uses discrete maneuver-plane rotation increments (RUI'NCT) to assign 

trial maneuver planes for the controlled airplane. FOm3CT is commonly referred 

to as the angle between maneuver planes. The value of RCYPNCT depends on the 

32 
APPENDIX A 

number of optional maneuver planes (NTILTT) per quadrant. Since the maneuverplane designations start at zero, the angle between planes is computed as 

During evaluations of the real-time control- and performancemodel programs, NTILTT was varied in an effort to assess its eff.ect on the maneuvering 

of the controlled airplane. The nominal value of NTILTT had always been 8, 

which established loo between maneuver planes. Performance evaluation showed 

that the maneuvering capabilities of the controlled airplane were degraded when 

the angle between maneuver planes was decreased below the nominal value (NTILTT 

was increased). As the angle was decreased, the trial maneuvers became more 

clustered about the maneuver currently being performed and about the plane 

nearest the opponent. Since this situation was undesirable, the angle between 

maneuver planes was increased in several small increments beyond the nominal 

setting until a more satisfactory value was determined. This occurred when the 

angle between maneuver planes was 12.86O (NTILlT = 6). Angles greater than 

this limited the airplane's capabilities by reducing the number of optional 

maneuver planes. Smaller angles caused too much clustering. 

Subroutine REACTT 

REACTT is executed at each decision interval (nominally 1 sec) to determine the most promising maneuver for the controlled airplane and to define the 

variables required by subroutine EQMOTT for execution of the selected maneuver. 

It utilizes the output of several other subroutines in accomplishing its purpose. At each decision interval, the opponent's flight path and attitude are 

extrapolated to a specified time in the future. Next, the controlled airplane's 

flight path and attitude are predicted for each of the trial maneuvers. The 

relative geometry between the extrapolated state of the opponent and the computed future state of the controlled airplane is then evaluated. Each maneuver 

is assigned a value resulting from the evaluation. The maneuver with the highest value is the one selected to be performed next. 

The changes made to REACTT range from minor logic manipulations to more 

complex logical evaluations and computations. For example, the segmentation 

in the subroutine has been completely eliminated. That is, it no longer 

requires four iterations of the program for canpleting the computations in the 

subroutine. The subroutine is now always completed in one iteration. The 

incorporation of the AML program on a faster computer made this possible and 

thus eliminated the delay in command to the controlled airplane. 

Currently, the decision interval is not constant. The time between decisions can now be greater than or less tham 1 sec, depending on the situation. 

Dive recovery is one situation which affects it. REACTT contains similar diverecovery logic to subroutine TRYNXT. Once the altitude of the controlled airplane is below 1066.8 m and its flight-path angle is greater than the recovery 

33 
APPEUDIX A 

angle, a dive-recovery maneuver is selected and the time counter for the standard decision interval is reset. The prevention of crashes between decision 

intervals is assisted by this logic. 

The decision interval time is also affected by the airplane's attitude. 

As previously discussed in subroutine E-T, there is an over-the-top problem. 

The airplane retains the previous comanded maneuver as long as this situation 

exists. This scheme prevents the selection of an improper maneuver and has 

proven to be very effective in handling the situation. 

Several other modifications were incorporated into this subroutine. These 

modifications are not only mre canplex than the ones previously discussed, but 

they are also more pertinent to specific areas of the maneuver selection process. Consequently, they have been categorized and are discussed in the following sections. 

Initial- maneuver selection.- FtEAC!"F saves the opponent's two previous positions for use in the future extrapolation of his position, velocity, and attitude. This presents samewhat of a problem at the beginning of the engagement 

since the two positions do not exist. Previous versions of the AML programs 

dealt with this problem by preventing the AML-controlled airplane fran selecting 

a new maneuver for the first 3 sec of the engagement. During this period of 

time, the opponent's positions were stored and the controlled airplane continued 

its initial input maneuver, which was generally straight flight. The technique 

sufficed for head-on initial conditions but was extremely unfavorable for the 

controlled airplane in sane other initial conditions. 

Currently, the opponent's previous two positions are canputed with the initial velocity components. Naturally, these computations aSsune that the opponent has been flying along a straight line with constant velocity. However, they 

permit a maneuver to be selected during the first iteration of the program, 

thereby enhancing the maneuver logic for all initial conditions. 

Undesirable extraElation detectis.- The extrapolation-detection logic 

identmiative situations that cause the extrapolation subroutine (EXTRT) 

to yield undesirable extrapolated variables for the opponent. The problem 

occurs when the opponent is in the rear hemisphere of the controlled airplane 

and the controlled airplane in the front hemisphere of the opponent. In this 

relative situation, it is possible for the AML to extrapolate the opponent's 

position to a point in front of the AML-controlled airplane. When this occurs, 

it is not very difficult for the opponent to stay behind since the trialmaneuver selection assumes the opponent is in front. The extrapolation is not 

in error. The relative velocity is such that the extrapolated range is greater 

than the range between the two airplanes. 

To correct this problem, the actual range between the airplanes is compared with an extrapolated range. If the extrapolated range is greater than the 

actual range, the opponent's position, velocity, and attitude are not extrap 

olated. The trial maneuvers are evaluated with respect to the wesent position, 

velocity, and attitude of the opponent. 

34 
I: 

APPENDIX A 

Variable prediction.- In conjunction with varying the opponent's extrapolation time as discussed in subroutine EX", the capability of varying the 

prediction time for the controlled airplane was installed in REACrT. Two situations exist for changing the prediction time from its nominal value of 2 sec. 

The time is changed to 6 sec when the range between the airplanes is greater 

than 1524 m and an undesirable extrapolation has not been detected. St is also 

changed to 6 sec for ranges dawn to a minimun of 304.8 m if the controlled airplane's deviation angle is less than 30° and the opponent's deviation angle is 

greater than 135O. Increasing the prediction time serves a purpose similar to 

that of increasing the extrapolation time of the opponent. 

Loaefactor .reducer.- With the exception of the soft-turn and low-energy 

recovery maneuvers, the AML trial maneuvers comnand the maximum load-factor 

capability of the controlled airplane. Consequently, for hard-turning fights, 

the amtrolled airplane will sanetimes have to resort to low-energy recovery 

maneuvers, forcing it to relinquish any angular advantage it might have 

achieved. To help prevent this problem, the selected trial maneuver is now 

reevaluated to determine if the commanded load factor can be reduced without 

affecting the value of the maneuver. 

The selected trial maneuver is reevaluated when the range between the two 

airplanes is greater than 1524 m, the controlled airplane is behind the opponent 

and the opponent is in front of it, straight flight has not been selected, and 

the controlled airplane is not in a dive recovery. When these conditions exist, 

two new trial maneuvers are set up, differing from the selected maneuver only 

in load-factor level. Their load-factor levels are 85 and 80 percent of the 

canmanded load factor, respectively. The new maneuvers are then evaluated and 

scored by the same process used for all maneuvers. The two are compared with 

the selected maneuver, and the maneuver with the highest value is selected. If 

two maneuvers have equal value, the one requiring the lesser load factor is 

selected. 

Subroutine STATE2 

Subroutine STATET evaluates the relative predicted and extrapolated state 

variables of the A"contro1led airplane and its opponent and assigns a numerical score for each trial maneuver. The score or value of a particular maneuver 

is obtained by answering a set of 14 questions covering specific areas and quantities of interest. These include angular relationships, distances, and velocities. The questions and a hypothetical. score are shown in the question set on 

page 7, with the AML-controlled airplane referred to as the attacker. The questions are worded in such a way that they can be answered by either "yes" or 

"no." The yes answers are assimed a nunerical value of 1 and the no answers 

are assigned 0. Once the evaluation is complete, the answers are sunmed for a 

nrrmerical score. The score is returned to the calling subroutine and stored for 

canparison with the other trial maneuvers. 

The question set on page 7 depicts the questions which are currently used 

by STATET. They differ fran the original questions in that one question has 

35 
APPENDIX A 

been deleted and three questions have been added. The deleted question evalw 

ated the specific energy rate of the controlled airplane. That is, once the 

controlled airplane's specific energy rate was less than -30.48 m/sec, a value 

of 0 was assigned to the question. This at times caused straight flight to be 

selected for the controlled airplane when other optional maneuvers were better 

for the current situation. The question was removed after real-time evaluation 

revealed that all the AML-controlled airplanes were able to perform as well and 

generally better without it. 

Three evaluation-type questions were added to STATET in an effort to 

enhance the maneuver selection process. Two of the questions canbine existing 

questions in the question array to form new questions. For example, looking 

at the question set on page 7, question 9 was worded so that it will be assigned 

a value of 1 when questions 1 and 2 are each assigned a value of 1. Likewise, 

question 1 2 will be assi gned a value of 1 when questions 3 and 4 are each 

assigned a value of 1. This places more emphasis on situations in which the 

AML-controlled airplane has a decided advantage over its opponent. The new 

questions are effective when several of the maneuvers have near equal values, 

with one or more having values of 1 on questions 1 and 2 and/or 3 and 4. When 

this occurs, the new questions will insure that one of the maneuvers with values 

of 1 on questions 1 and 2 and/or 3 and 4 will be selected. 

Question 11 was added to the question array to assist in meventing 

crashes. It keys on predicted altitude of the AML-controlled airplane. For 

maneuvers that have a ~edicted altitude of less than 91 .44 m, a value of -13 

is assigned to the question. Therefore, the highest value these maneuvers can 

have is 0. When this question is answered yes, a value of 0 is assigned to it 

so it will not affect the maneuver selection process. 

Subroutine THRWLT 

Subroutine THROTLT is called each iteration of the AML programs to determine the proper throttle setting for the AML-controlled airplane. The throttle 

setting is designated by the variable TPOST. TPOST is set equal to 0, 1, and 2 

for idle, military, and afterburner thrusts, respectively. Although all the 

trial maneuvers designate afterburner thrust, this subroutine can change the 

throttle setting if ~e AML-controlled airplane's variables and other geometric 

aondi tions warrant it. 

The logic which defines the situations for changing the throttle setting 

is canpletely different fran previous versions of the AML programs. An important feature of the present version of the subroutine is that it now uses 

corner velocity for determining the throttle setting in sane situations. The 

corner velocity, as previously defined in subroutine AERF4, is the Mach number 

at which the controlled airplane can achieve its maximun turn rate. It is COIP 

pared with the airplane's Mach nuaber to determine the throttle setting when 

the range is less than 1 828.8 m and neither the AML-controlled airplane nor the 

opponent has achieved a good tracking solution. The logic is designed to set 

the throttle to idle, military, or afterburner, depending on the speed of the 

controlled airplane. If the airplane exceeds its corner velocity by 20 percent, 

36 
APPENDIX A 

the throttle is set to idle. Likewise, if the airplane is 10 percent below its 

corner velocity, the throttle is set to afterburner position. Military mer 

is used when the airplane's speed is between the specified levels. 

Another section of the throttle logic is designed to prevent the AMLcontrolled airplane fran overshooting the opponent once it achieves a good 

angular relationship. In this section, a delta range is calculated by using 

the controlled airplane's total acceleration, range rate, and distance fran 

the opponent. The delta range serves as a prediction of an overshoot situation. If the situation exists, the throttle is set to idle. 

The last section of the throttle logic deals with the reverse of the previous situation. It was designed to cause the opponent to overshoot once he 

achieves a gmd angular relationship and is close behind the controlled airplane 

with a high closure rate. Once again, the idle setting is used when this condition exists. 

Subroutine TRYNXT 

Subroutine TRYNXT sets up trial maneuvers for evaluation at each decision 

interval in the AML programs. The number of maneuvers can vary between 1 

and 10, depending on the state of the A"-controlled airplane and its relative 

situation with the opponent. The state of the controlled airplane determines 

the types of maneuvers to be set up. The maneuvers are divided into the categories of dive recovery, low-speed recovery or energy conservation, and normal 

conditions. Dive recovery and energy conservation have priority over the normal 

condition maneuvers. They set up trial maneuvers that are designed to either 

prevent the airplane fran crashing or from losing so much energy that it can 

no longer maneuver effectively. If neither of these conditions exist, the sitw 

ation is considered to be normal and trial maneuvers are set up based primarily 

on maneuver-plane rotations that are defined by the controlled airplane's 

velocity vector and the extrapolated opponent's position. 

The current version of this subroutine contains several modifications. 

The order of priorities for the categories remains unchanged. However, each 

category contains either immoved logical constraints or additional trial 

maneuvers. The diverecovery section, first in priority, currently contains 

fewer maneuvering constraints for the prevention of crashes. The dive angle 

is not monitored until the controlled airplane flies below 1066.8 m. Below this 

level, the airplane is commanded to pull up in a vertical plane once its dive 

angle reaches the maximum angle for recovery. These tests are performed at 

each decision interval in the program. The same tests are performed in subroutine REACTT to detect crash situations between decision intervals. If one 

is detected, TRYNXT is called and a dive-recovery maneuver is set up. 

The energyaonservation section is another area in which the logical constraints have been imp-oved. The modifications may seem minor upon initial 

investigation; however, real- time performance evaluation of the controlled 

models revealed that they are very important to the overall operation of the 

maneuver logic. 

37 
APPENDIX A 

The energy-conservation section has the second highest priority to dive 

recovery. Its trial maneuvers are dependent on maximum available load factor 

and the pitch angle of the velocity vector, the load factor being the more 

important variable. Once the maximum available load factor, which is canputed 

from the maximum C,, decreases to specified levels, trial maneuvers are set 

up which are designed to assist the controlled airplane in regaining energy. 

The load-factor levels were initially assigned values of 1.5 and 1.0, respectively. Real-time evaluation revealed that these designated levels placed too 

much restriction on the maneuvering capabilities of the controlled airplane. 

Currently, the load-factor levels are assigned values of 0.5 and 0.25, respeo 

tilmly. These levels permit a greater degree of maneuvering freedom and at the 

same time allow the airplane to recover fran stall situations. 

The energy maneuvers controlled by the velocity-vector pitch angle were 

redefined in conjunction with the newly specified load-factor levels. Originally 

the controlled airplane was commanded to continue pulling maximun load in the 

maneuver plane nearest the opponent if its load factor had fallen below the 

specified level and the flight-path angle was greater than 80°. It was discovered that the airplane could not always successfully perform this maneuver. 

In many instances, its energy level became so critical that all positional 

advantages, if any, were relinquished. This logic has been eliminated. 

The final category in which modifications were made to TRYNXT is the 

normal-condition section. Several trial maneuvers were added to the existirlg 

maneuvers in this section. These maneuvers not only increase the maneuvering 

freedan of the controlled airplane but also refine the maneuver selection process. An example of the refinement is depicted by the utilization of the softturn maneuver. This maneuver, as in previous versions of TRYNXT, is a trial 

maneuver which will approximately result in a trajectory intercepting the opponent at its extrapolated position. The sof t-turn maneuver is currently the only 

maneuver set up for the controlled airplane when its deviation angle is less 

than 40° and the opponent's deviation angle is greater than 1 20°. This constraint forces the controlled airplane to perform what is considered the best 

of the trial maneuvers once it has achieved a fairly good tracking solution. 

Four other trial maneuvers were added to TRYNXT for evaluation during normal conditions. Three of the trial maneuvers have their maneuver planes defined 

in 90° intervals with respect to each other, with the maneuver plane nearest 

the opponent being the plane of reference. That is, their respective maneuver 

plane rotations are 900, 1 80°, and 270° away from the plane nearest the opponent. These maneuvers prove beneficial once the tactical position of the AMLcontrolled airplane deteriorates to a level at which all the trial maneuvers 

have a low value assigned to them. The controlled airplane is often in this 

situation after a dive recovery or low-energy recovery. Its state variables 

are such that it cannot achieve much success by performing the other trial 

maneuvers. These three maneuvers provide what could be considered alternatims 

in this situation. 

The other trial maneuver added to the normal-condition section is a diverecovery maneuver. This maneuver instructs the controlled airplane to pull up 

in a vertical plane with maximlm load factor. The selection of the maneuver 

fran this section could prevent a critical situation in which the airplane 

38 
APPENDIX A 

would need more time for recovery. It generally will not be selected from the 

normal-condition section; however, it is felt that the airplane should have the 

option of performing this maneuver since crashes have always been sanewhat of 

a problem in the AML programs. 

39 
APPENDIX B 

REAL-TIME IMPLEMENTATION AND mFINEMENT OF AML CONTROL M3DEL 

A control system which converts AML cummands of bank angle and load factor 

to appropriate aileron, spoiler, stabilator, and rudder comnands was developed 

for the F-4 airplane (as simulated on the Langley differential manuevering simulator (DMS)) by Decision Science, Inc. (DSI) under contract to Langley Research 

Center and was delivered in the form of a tatckprocessing computer program. 

A more detailed description of this program and its design philosophy may be 

found in reference 4. 

At Langley, a nunber of program evaluations and refinements have been made. 

The system's ability to execute many typs of very demanding command sequences 

was studied, along with conditions under which it might be unable to prevent the 

controlled airplane fran departing. As expected, large changes in bank angle at 

high pitch angles were often either performed at very low roIl rates or the airplane departed. To produce more desirable responses, it was decided to use a 

quasi-sequential scheme of roll and then pitch to effect these maneuvers. This 

problem and the procedures for handling it will be discusssed in more detail 

later. 

When the Control Data CYBER series 175 computer became available for use 

with real-time simulations, the control model was interfaced with the basic DMS 

program. Prior to this time, computing capacity had been insufficient to do it. I 

The control model was designed to replace, in a modular form and, as nearly 

as possible, on a one-for-one basis, the performance model already operating in 

real time. This was done to have the program ready as soon as the new canputer 

became available and to minimize real-time checkout. Because of this, however, 

the AML-DMS program is not as efficient as it could be. Much tabulated aerodynamic data, as well as routines for solving equations of motion, could be shared 

with the piloted airplane. 

Concurrent with its implementation in real time, the maneuver logic and 

other AML functions independent of the control system itself were updated to 

the level of the latest performance-model program. Most of these have already 

been covered in appendix A and will not be discussed again. 

In its first real-time flights, the control model did fly although it 

often mashed or departed and was of almost no canpetition to the human pilot. 

It was only through a long, drawn-out, mostly trial-and-error process in real 

time that the program was refined to be the super canpetitor that it is. 

Observing the real-time operation of the system was the only practical means to 

gain the needed insight to refine it. Otherwise, major deficiencies remained 

obscure and difficult to detect although they were often corrected by relatively 

simple modifications. In this perspective, then, the system designed by DSI was 

a good one which remains mostly intact. The company did not have access to 

facilities which could thoroughly exercise the program against a human pilot and 

in fact, at the time, neither did Langley Research Center. 

40 
APPENDIX B 

Sane improvements which pertain strictly to the maneuver logic and not to 

the functioning of the control system itself were also devised during this 

time. These were also included in the performance model and are discussed in 

appendix A. 

The same problem which had for so long plagued the perf ormancemodel pcogram was present with the control model, though in a sanewhat different form. 

The over-the-top problem was discussed in appendix A. With the control model, 

much of the problem was eliminated by simply referencing the maneuver-plane 

axis system to the X body axis of the airplane rather than to the velocity 

vector. Having a full canplement of manent equations, the control model, 

unlike the performance model, does not depend on velocity-vector-based mathematical approximations to drive its attitude or to determine what its attitude 

is. Thus, it requires maneuver-plane concepts only to choose new maneuvers. 

A body-axis-based maneuver-plane system has worked well for this purpose. The 

primary program changes to make the maneuver-plane-axis-system conversion were 

made in subroutines REACTJ! and TRYNXT. In TRYNXT, the rotation about the maneuver plane X-axis to the plane containing the opponent (ROTT) is computed by 

using body angles rather than velocity-vector angles. Also, in the calls to 

NORPLN to get maneuver-plane normals, body Euler angles are now used. The subroutine NORPLN has been itself simplified to use these Euler angles directly 

to compute the direction cosines of the maneuver plane Y-axis. These are equivalent to the required inertial ccmponents of the maneuver-plane unit normal. 

Although the maneuver-plane-axis-system change allowed the quaternions 

to do their job without problems of Euler angle incanpatibility, a minor problem still remained. when the X body axis (now also the maneuver plane X-axis) 

transitioned through a pitch angle of 900, the reference with respect to which 

the commanded bank was determined changed. The problem was solved simply by 

evaluating and choosing a new maneuver whenever this occurred. In subroutine 

GETOOM, the pitckangle transition is detected by a change of 1 80° in body yaw 

between two consecutive program iterations, and a flag is set. This flag signals REACTT to initiate an immediate new maneuver selection by using the new 

r ef er ence. 

As previously mentioned, under certain conditions the airplane had very 

poor roll response, as well as a tendency to depart. In general, these conditions were characterized by a low airplane energy state, high airplane pitch 

angle, high angle of attack, and a large comnanded change in roll angle. The 

general approach to solving the problem was to trade normal acceleration for 

increased roll response by using a modified roll-thewpitch sequence to effect 

commands. Even in the absence of the previously mentioned problems, it may be 

reasonable to attempt to have roll changes lead :(i.e., be canpleted slightly 

before) pitch changes. This is especially true where the desired change in 

roll angle is large and the desired change in pitch angle is positive. If it 

is assuned that there is only one direction, in terms of a rotation about the 

airplane's body axis, in which a lift vector of given magnitude should be 

directed to execute each elemental maneuver, then directing this vector fully 

along the interim path of bank is probably undesirable. Energy may be wasted 

and the airplane's path of flight temporarily curved in a direction other than 

that upon which the maneuver's choice was based. This is particularly true of 

41 
APPENDIX B 

the AML because of the way it evaluates and selects maneuvers. No consideration 

is given to transition banking. For evaluation purposes, it is assumed that the 

airplane's flight path will be planar over the duration of the evaluated maneuver, beginning at the specified bank angle. Thus, lift used before achieving 

the selected bank angle reduces the validity of the maneuver evaluation logic. 

Primarily the modified process of carrying out AML camnands was implemented 

in subroutines AUGCONT, GETCUM, and LOCNTR. Because AUGCONT is an entirely new 

subroutine, it is included in a more detailed separate section which follows the 

present one. In GETCOM, provisions were made to reduce the comanded angle of 

attack during large commanded roll changes without regard for the remaining airplane state variables. No action is taken if the comanded roll change is less 

than 300. If it is greater, the angle of attack (consequently the load factor) 

is reduced in proportion to the amount that the commanded roll change exceeds 

300. The canputations which reduced angle of attack for "violent roll maneuvers" in subroutine LOCNTR have been removed. The change in GETCOM is less 

restrictive but covers the problems previously handled by this logic. 

Subroutine AUGCONT 

Except for the subroutine AMLVS3 which interfaces the AML control-system 

model with the basic real-time DMS computer program, the subroutine AUGCONT is 

the only entirely new subroutine added to the control-system program. The purpose of the routine is to insure that the commanded bank changes will & performed at some minimum roll rate. For a given Mach and altitude condition, the 

effectiveness of the ailerons and spoilers can generally be increased by lowering the angle of attack. Under certain circumstances, the rudder may also Be 

used to increase roll response. However, care must be taken in the use of the 

rudder to prevent departures. 

Since both load factor and bank angle are essential to air-combat manewering, the wisdom of trading normal acceleration for improved roll response may 

be questioned by some. The philosophy on which this routine is based is that 

unless the load factor desired is oriented in the proper direction, or very 

nearly so, it accomplishes very little and may actually have a negative tactical effect. 

The trade-off is not always made, but only in circumstances in which roll 

authority has decreased to a predetermined minimum. 

If the desired change in roll is less than 5O, the routine does nothing. 

If the desired change is greater than 50, an estimate of the time te 

required to complete the change (assuming a minimum desired roll rate of 

20°/sec) is calculated by 

where oc is the commanded bank angle and +p is the present bank angle. 

42 
APPENDIX B 

Then assuming the ailerons and spoilers to be deflected to their maximms 

in a direction to reduce the roll error, the maximum rolling acceleration that 

they can generate for the present Mach number, altitude, and angle of attack is 

canputed fran the following equation: 

The estimated roll-angle change A~E that would result from using this 

rolling acceleration over the time te is given by 

The estimated roll change is canpared with the actual desired roll change 

(@c - 6 1. If A~E is greater, no action is taken. Otherwise, the commanded 

angle of attack is reduced by 5O and A6E is recanputed and again canpared 

with the desired roll change. The process is continued until sufficient rolling acceleration is obtained to produce the desired roll change in time te 

or until the commanded angle of attack has been reduced to zero. 

If reducing the angle of attack to zero still does not provide enough roll 

authority, consideration is given to using the rudder to effect the roll. If 

both the angle of attack and angle of sideslip are each below 15O, the rudder 

is deflected to its maximum in the direction to produce the desired roll. This 

is accanplished by setting a flag which signals the subroutine LACTIW to actually set the rudder deflection. 

This routine is executed during every program iteration (1/32 sec). The 

original angle-of-attack command is restored before entering the routine on 

successive iterations. Thus, the amunt, if any, of angle-of-attack reduction 

is continuously updated. Likewise, the use of rudder is reevaluated during 

each iteration. 

43 
1 . Burgin, George H.; Fogel, Lawrence J.; and Phelps, J. Price: An Adaptive 

Maneuvering Logic Canputer Program for the Simulation of One-on-One Airto-Air Canbat - Vol. I: General Description. NASA CR-2582, 1975. 

2. Burgin, George H.; and Owens, A. J.: An Adaptive Maneuvering Logic Canputer 

Program for the Simulation of Oneon-One Air-to-Air Canbat - Vol. 11: 

Program Description. NASA "2583, 1975. 

3. Hankins, Walter W., 111: Interactive Canputerized Air Canbat Opponent. 

Flight Simulation/Guidance Systems Simulation, Am-(2-1 98, June 1976, 

pp. 21-1 - 21-9. 

4. Burgin, George H.; and Eggleston, David M.: Design of an All-Attitude Flight 

Control System To Execute Comnanded Bank Angles and Angles of Attack. NASA 

CR-145004, 9761 . 5. Ashworth, B. R.; and Kahlbawn, William M., Jr.: Description and Performance 

of the Langley Differential Maneuvering Simulator. NASA TN D-7304, 1973. 

44 
TABm I.- TOA AND AMLS VALUES FOR PILOT-VERSUS-PILOP RUNS 

% "I 

TOA and AMLS values for pilot-versus-pilot runs for - 

Sphere A I 

, Run Sphere B 

Pilot TOA 

sec 

Pilot TOA 

sec 

AMLS t 

1 

2 

3 

4 

a5 

6 

7 

a8 

9 

a1 0 

11 

"1 2 

13 

14 

15 

16 

17 

al 8 

a1 9 

a20 

21 

a22 

a23 

24 

C 

C 

C 

C 

C 

C 

F 

F 

F 

E 

E 

E 

E 

D 

D 

D 

9 

0. 

38. 

41 .5 

8.6 

13. 

23. 

.6 

3.5 

43.3 

108.5 

93. 

126. 

81 .5 

7.0 

36.5 

I D l 33. 

Mean . . . . . Standard 

deviation . . aData extrapolated. 

24.3 

3.6 

104.5 

1 01 . 106. 

.53 

11.5 

42.4 

42.3 

4.2 

3.1 

5.4 

5.3 

3.9 

5. 

4.4 

3.4 

3.8 

5.8 

6.6 

6.3 

6.8 

6. 

4.6 

5.3 

5.4 

4.5 

4.3 

4.4 

6.5 

6.1 

6.5 

4.8 

5.1 

1.07 

D 

D 

F 

F 

E 

E 

C 

D 

D 

D 

C 

F 

F 

F 

F 

C 

C 

E 

E 

E 

C 

C 

D 

E 

84. 

1 46.5 

1 2. 

19.5 

107.3 

19.5 

94.5 

98.5 

2.1 

0. 

1.7 

12.5 

11.5 

51.5 

17. 

9.5 

72.3 

76.2 

65.5 

0. 

2.2 

10.6 

53. 

45.23 

44.71 

118. 

6.2 

7.6 

4.8 

4.8 

6.2 

5.2 

6.0 

7.1 

6.7 

4.5 

3.9 

4.1 

3.6 

4.1 

5.6 

5. 

4.8 

5.9 

6.1 

5.9 

3.7 

4.2 

4.1 

5.4 

5.23 

1.11 
Uot 

- 

C 

D 

COA each 

run, sec 

Mean 

3.5 

0. 0.67 

4.0 1 

13.5 

0. 

0. 

35.5 ' 

22. 12.5 

standard 

ieviation 

TABLE 11.- TaA DATA AND STATISTICS 

(a) Pilot-versut-control-model runs 

1.4 

14.18 

E 5.5 

2.0 

3.0 

0. 

112.5 

104.5 37.92 54.76 

F 0. 

5.0 

0. 

24. 

46.5 

ilot 

- 

AML 

COA each 

run, sec 

47. 

68. 

77.5 

77. 

44. 

82.5 

27. 

18.5 

68. 

131.5 

18. ' 39.5 

82.5 

32.5 

91 .5 

35. 

9.5 

2.5 

54.5 

95. 

90. 

56.5 

64.5 

41 .O 19.42 20.89 45.0 

1, 

Mean 

66 

50.42 

itandard 

ieviation 

16.58 

43.87 

42.25 37 

67.58 68.58 

i tuden t ' s 

t 

9.62 

2.01 

.16 

1.645 I 

)egr ees 

freedom 

of 

5 

6 

10 

6 

Confidence 

level of 

mean difference, 

percent 

99.5 

95 

55 

90 

I* i17.63, 31.32 Lt 99.5 
?ilot 

C 

D 

E 

F 

All 

COA each 

run, sec 

0. 

3.5 

0. 

0. 

1.5 

0. 

18.5 

11. 

10. 

0. 

76. 

121.5 

10. 

16. 

1. 

24. 

0. 

24.5 

5. 

0. 

2.5 

48.5 

29.5 

33.0 

lean 

0.83 

39.5 

12.58 

19.75 

18.17 

TABLE 11.- Concluded 

(b) Pilot-versus-performance-model runs 

: tandar d 

leviation 

1.44 

48.46 

10.8 

20.01 

28.8 

kilot 

AML 

V 

M3A each 

run, sec 

91 . 

92 

91 .5 

51.5 

54.5 

86. 

29. 

55.5 

10.5 

83.5 

3.5 

0. 

18. 

6. 

35. 

29. 

65. 

45.5 

10. 

79. 

26.5 

8.0 

15.5 

5.5 

Mean 

77.7! 

30.3: 

33.01 

24.01 

41 .3' 

Standard 

-.Ideviation 

19.32 

33.1 5 

20.76 

27 . 92 

32.5 

tudent' s of Degrees 

freedom 

9.74 

.38 

2.15 

.31 

2.6 

5 

10 

8 

11 

47 

Confidence 

level of 

lean difference, 

percent 

99.5 

60 

95 

60 

99.5 
TABLE 111.- AMLS DATA AND STATISTICS 

?ilot 

- 

C 

D 

E 

F 

All 

- 

WLS 

Each 

tun 

- 

4.6 

4.4 

4.2 

4.3 

4.6 

3.9 

4.8 

5. 

4.3 

3.6 

5.2 

4.9 

4.4 

4.8 

4.1 

4.4 

6.6 

6.7 

4.3 

3.8 

4.4 

4.7 

4.8 

5.2 

lean 

- 

4.33 

4.63 

5.1 7 

4.53 

- 

4.67 

Standard 

leviation 

0.26 

. 59 

1.17 

.48 

.73 

(a) Pilot-versus-control-model runs 

- 

'ilot 

A 

11, 

, 

I 

L 

,Id 

AMLS 

each 

run 

5.8 

5.7 

6. 

6.1 

5.8 

6.4 

5.6 

5.3 

6. 

7.1 

5. 

5.4 

5.7 

5.3 

6. 

5.8 

3.9 

3.8 

6.1 

6.8 

6. 

5.8 

5.5 

5.2 

Mean 

- 

5.97 

5.73 

5.08 

4.9 

m 

5.67 

, 

1 

. 98 \ 

1 

1 

. 55 

10.9 

2.82 

.15 

4.57 

4.76 

Iegr ees 

Ereedan 

of 

12 

11 

12 

12 

48 

Confidence 

level of 

mean difference, 

percent 

99.5 

99 

55 

99.5 

99.5 
AMLS 

Pilot each Mean 

run 

"- 

c 4.3 

D 

E 

F 

Al 

4.1 

4.2 

4.5 

4.5 

4.1 

5. 

4.8 

5.2 

4.2 

5.8 

6.9 

4.9 

5.3 

4.7 

4.8 

4.3 

5.0 

5.1 

4.4 

4.8 

5.6 

5.0 

5.4 

- 

4.28 

5.32 

4.83 

5.05 

4.87 

- 

(b) Pilot-v6rsus-performance-model rms 

AMLS 

run 

Pilot each Mean deviation, Standard Student's 

leviation t 

0.1 8 

.93 

.33 

. 43 

.64 

A L 6.1 

6.4 

6. 

5.9 

5.8 

6.3 

5.4 

5.7 

5.2 

6.2 

4.5 

3.9 

5.3 

5.1 

5.6 

5. 

5.7 

5.4 

5.4 

5.9 

5.6 

4.8 

5. 

4.9 

- 

6.08 

5.15 

5.35 

5.27 

5.46 

- 

0.23 

.83 

. 27 

.44 

-6 

15 

.33 

2.99 

.88 

3.28 

-lr 

Degrees 

of 

Ereedm 

11 

12 

12 

11 

45 

Confidence 

level of 

mean difference, 

percent 

99.5 

60 

99 

,80 

99.5 
UI 

0 

mLE Iv.- HISSILE AND m-zm mIEs 

(a) Pilot versus pilot 

r Sphere A Sphere B 1 

Run r Pilot 

Times of first entry r Average time in zone t Times of first entry Average time in zone I 

Pilot 

D 

D 

P 

P 

E 

E 

D 

C 

D 

D 

C 

P 

P 

P 

C 

P 

C 

E 

E 

E 

C 

C 

D 

E 

I zone A 1 zone B Zone A GUnS Zone B 

30. 

86.5 

5.5 

19. 

10. 

23. 

26. 

20.1 

12. 

0 

1.5 

2.82 

0 

0 

7.5 

0 

7.5 

29.9 

27.18 

10.9 

0 

0 

0 

1.25 

GUnS 

"" 

"" ""_ 

"I 

"" 

""_ 

"" 

"" 

"- "" 

171. 

147. 

75. ""_ 

"" ""_ 

zone A 

116.5 

56.5 

14. 

28. 

35.77 

33.5 

63. 

35. 

33.5 

2.13 

7.5 

9.02 

1.5 

3. 

19.5 

9. 

16. 

55.6 

42.1 

18.8 

7.5 

2.77 

6.86 

19. 

Zone A 

8. 

0 

46.5 

44.5 

39. 

2.86 

3.5 

5.84 

13. 

47.6 

78.95 

79 * 

56.5 

26. 

6. 

12. 

13.2 

41.5 

18.2 

15.45 

81 * 

37.1 8 

69.85 

26.5 

Zone B 

5. 

0 

28. 

14. 

0 

20.5 

0 

0 

0 

26.99 

63.1 6 

34.5 

48.5 

9. 

0 

0 

0 

9.5 

0 

3. 

13.87 

55. 

42.4 

16.5 

Zone B 

36.5 

20.5 

136. 

22. 

29.5 

46.5 

19. 

21 . 78.5 

22. 

32.5 

""" 

""- ""- 

26.5 

55. 

32.5 

27. 

24. 

"" 

"- 

"I_ 

20. 

"" 

Guns 

"" 

"" 

"" ""_ 

""- "" 

"" 

"" 

"" 

"" 

6. 

12.4 

26.5 

"" 

I" 

I" 

"" 

"" 

I" 

"" 

.5 

3.74 

"" 

"- 

1- 1 

2 

3 

4 

6 

5 

7 

8 

9 

11 

10 

12 

13 

14 

C 

C 

C 

C 

C 

C 

P 

P 

P 

E 

S 

S 

E 

D 

D 

D 

D 

P 

D 

D 

P 

S 

E 

P 

31 . 

16. 

20. 

12.5 

16. 

20.5 

19. 

18. 

20.5 

17. 

22. 

23.5 

66. 

22.5 

36. 

48. 

24.5 

33. 

24. 

36. 

32. 

39 s 

18.5 

"" 

27.5 

19. 

17. 

20. 

26.5 

44. 

16.5 

19. 

25. 26. 20. 

29. 

13. 

72. 

25. 

41.5 

51 . 31. 

25. 

22.5 

39. 

16.5 

18. 

18.5 

70. 

100.5 

"" ""- 

120. 

75. 

92.5 

153. 

"" 

180. 

""" 

17.5 

113. 

"" 

17.5 

I"_ 

"" 

"" 

23. 

18.5 

24. 

24.5 

" 

66. 

""- 

""- 

17.5 

1.5 "- "- 

"" , "- 

"" ""- 

"" "- ""_ 

"" 

"- 

"" 

"" 

91. 

158.5 

135. 

"" 

" 

" "- 

' 15 

16 

18 

17 

.19 

20 

21 

' 24 

23 

' 22 

99. 

' ""- ""_ "" 

- "" 

44.5 "" 

36. 48. 

34. 

I 42. 92.5 

21 . " 

"" 

11.19 

9. 

9.1 

"- "- 

"- "- -. c-", 

Amrage . . . 33.15 32.17 16.25 27.6 99.19 , 13.36 

Average 

., .. L, 

of both 

spheres . . 30.37 89.37 , I 29.34 ' 14.81 ~ ~ 

" 

I 
1 1 1- I. Avera I time in zone 

Zone A 

59.5 

63.5 

35.5 

35.5 

48.5 

71. 

51 . 42. 

123.5 

53. 

46.5 

67.5 

43.5 

74.5 

35. 

16. 

19. 

36. 

43. 

41. 

21.5 

3. 

30.5 

6.5 

44.44 

1- 

ZoneB J- GunS 

I1 71. "- , 

" 

- 

103. 

"" 

"" 

"" 

"" 

" 

"" "- 

165.5 

-I- "- 

" "- 

"" 

"" "- "- "- 

" 

1 

2 

'3 

4 

5 

6 

7 

8 

10 

9 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

- 24 

25. 

. 28.5 

18.5 

19.5 

19. 

16.5 

16.5 

32. 

41. 

24. 

29.5 

23.5 

22.5 

63. 

52.5 

19.5 

18.5 

20.5 

15. 

31. 

17. 

16. 

24.5 

"" 

28. ' --- 

21. "" 

' 31 . "- 

7.5 

23. 

33.5 

38.5 

12. 

8.5 

26.5 

25.5 

16. 

27. 

20. 

31.5 

16. 

10.5 

9.5 

41. 

29. 

5.5 

14.5 

31.5 

38.5 

37.5 

42. 

"" 

12. 

10.5 

33. 

8.5 

22.5 

43. 

26. 

11. 

13. 

79.5 

18.5 

25. 

13.5 

39.5 

18.5 

2.5 

19.5 

4.5 

15.5 

19. 

2. 

0 

1.5 

4. 

I 

I 

D 

0 

I 

I 

C 

C 

D 

D 

C' 

C 

C 

C 

E 

E 

D 

I 

E 

I 

D 

E 

E 

I 

1.5 

12. 

14.5 

19. 

1.5 

1.5 

9.5 

4.5 

,, ' 

14. 

"- 

10. 

14. 

14.5 

7. 

2.5 

0 

16. 

17.5 

2.5 

3.5 

2.5 

21. 

28.5 

19.5 

28.5 

20. . 

36.5 

22. 

22.5 

18. 

16. 

63. 

28.5 

42.5 

23. 

25. 

28. 

56.5 

142. 

23. 

168. 

31. 

17.5 

23.5 

33. 

36.5 

25.5 

46.25 

"" 

21 . 

19. 

33. 

18. 

18. 

17.5 

15.5 

26.5 

31 . 26.5 

19.5 

21. 

19.5 

22.5 

29. 

18.5 

18. 

19. 

20. 

16. 

30. 

19. 

18. 

21.5 

~. 

79.5 --- 

18. 

21.5 I .I"- 

33. 

36.5 ---- 

63.5 ---- 

"- 

" 

""_ "_ 

25.5 ' --- 

31. , -" 

"- "" 

"" 

" 

"" 

97.5 

" 

24. 

178. 

26.5 

"- 

21.5 

60.5 

16.5 

21.5 

139. 

18.5 

101. 

18. 

57.23 

"- 

72.5 

105. 

" 

Averacra . . . 32.23 22.71 9.88 18.44 
(c) Pilot-versus-An, prforuna de1 

Sphere A I Sphere B 1 I T Average tiu in zone I T Tim of first entry r Amrage time in zone Pilot a- Pilot 

D 

D 

c 

c 

C 

c 

E 

c 

D 

E 

D 

? 

? 

? 

L 

c 

D 

D 

E 

? 

L 

L 

? 

? 

" 

2one A 

21 -5 

18. 

27.5 

35.5 

20.5 

29. 

20.5 

14.5 

15. 

32. 

16.5 

16. 

15. 

16.5 

19. 

17.5 

30.5 

29. 

16. 

40.5 

33.5 

32.5 

30.5 

26.5 

?one A mne B ZOllcB ?one A 

8.5 

12.5 

7.5 

13. 

0 

21 

4.5 

10. 

18.5 

15. 

3.5 

17. 

1.5 

30. 

3. 

.5 

15.5 

21.5 

2.5 

0 

0 

48.5 

8. 

0 

Zone A BonaB 

27. 

27.5 

11. 

20.5 

5. 

32.5 

23.5 

22. 

41.5 

36. 

13. 

38.5 

7. 

51.5 

7. 

17. 

41 . 

59. 

14.5 

0 

0 

22.5 

75. 

10. 

1 

2 

4 

3 

5 

6 

7 

9 

8 

11 

10 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

54.5 

20. 

40.5 

30.5 

30.5 

27. 

28. 

16. 

16. 

28. 

51 . 

16.5 

16. 

17.5 

22. 

26.5 

61. 

33. 

17. 

58. 

22.5 

80. 

32.5 

0 

31. 

28.5 

17.5 

32.5 

33. 

18. 

90. 

18. 

40.5 

177.5 

24.5 

69. 

29. 

" 

19. 

"- 

33. 

" 

28. 

23.5 

22.5 

39.5 

39.5 

25. 

26. 

31.5 

16.5 

16. 

36.5 

18. 

55.5 

16.5 

21. 

38.5 

19. 

0 

36.5 

19. 

38.5 

47. 

41 . 53.5 

65.5 

62. 

53. 

67.5 

39. 

33. 

30.5 

25.5 

71 . 

38.5 

58.5 

23.5 

56.5 

38.5 

7s 

11. 

32.5 

15. 

35. 

35.5 

17.5 

i 22. 

24.5 

24.5 

17.5 

36. 

20. 

16.5 

9.5 

12. 

8.5 

33.5 

19. 

15.5 

6.5 

26. 

15.5 

0 

1. 

13.5 

5.5 

1 

"- 

30.5 

25.5 

26.5 

17. 

12.5 

12.5 , 6.5 

4.5 

35. 

33.5 - 

29.5 - 

- 

Amrage . . . 41 .42 66.76 25.1 , 10.9 

" 

" 
Rm 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

TABLE V.- OF'FmIVE AND DEFENSIVE AML SCORES 

(a) Pilot versus pilot 

Sphere A 

Offensive Defensive Pilot AMIS ,AMT.S 

AMLS 

C 

C 

C 

C 

C 

C 

F 

F 

F 

E 

E 

E 

E 

D 

D 

D 

D 

F 

D 

D 

F 

E 

E 

F 

Average . . 

0.8 

.2 

1.6 

1.5 

.6 

1.3 

.7 

e4 

.6 

1.8 

2.5 

2.3 

2.8 

1 e9 

.9 

1.5 

1.6 

1.0 

.8 

.9 

2.5 

2.1 

2.6 

1 .o 

1.41 

3.2 

2.5 

3.6 

3.7 

3.1 

3.6 

3.3 

2.7 

3.0 

3.8 

3.8 

3.8 

3.7 

4.0 

3.5 

3.7 

3.7 

3.3 

3.3 

3.3 

3.9 

3.7 

3.6 

3.5 

3.47 

3.9 

2.8 

5.2 

5.2 

3.7 

4.9 

4.1 

3.2 

3.6 

5.6 

6.3 

6.1 

6.5 

5.9 

4.3 

5.2 

5.3 

4.3 

4.1 

4.2 

6.4 

5.8 

6.2 

4.5 

4.89 

" 

.- 

1- 

Sphere B 

- 

Pilot AMLS Off ens ive 1- 

D 

D 

F 

F 

E 

E 

C 

D 

D 

D 

C 

F 

F 

F 

F 

C 

C 

E 

E 

E 

C 

C 

D 

E 

2.1 

3.4 

1.1 

1.2 

2.2 

1.4 

2.0 

2.9 

2.4 

.9 

.5 

.6 

.5 

.7 

1.6 

1 -1 

1.1 

2.1 

2.1 

2.0 

.6 

.5 

-6 

1.6 

1.47 

Defensive 

AMLS AMLS 

3.8 

3.6 

3.5 

3.8 

3.7 

3.6 

4.0 

4.0 

3.5 

3.2 

3.3 

3.2 

3.3 

3.8 

3.7 

3.6 

3.6 

3.8 

3.8 

3.0 

3.3 

3.2 

3.6 

3.55 

3.8 

5.9 

7.2 

4.7 

4.6 

6.0 

5.2 

5.6 

6.9 

6.4 

4.3 

3.7 

3.9 

3.7 

4.0 

5.4 

4.8 

4.7 

5.7 

5.9 

5.8 

3.6 

3.9 

3.8 

5.2 

5.02 
TABLE V.- Concluded 

(b) Pilot versus AML control model 

? 

Sphere A 

AMLS AMIS AMLS Pilot 

Sphere B b 

Run Offensive Defensive Off ens ive Pilot AMIS 

! 

):I D" I 12 F 

5 '1 F 

16 F 1;' IC C 

9 D 

;lo , D 

'11 ' c 

12 C 

l3 14I c" 

15 1 E 

16 ~ E 

18 , F 

,I7 ~ D 

~ 23 

24 

Average . . 

.7 

1.2 

1.4 

.6 

1.2 

1 .o 

.9 

.9 

.3 

.9 

.8 

1.1 

.7 

.7 

1.5 

1.3 ! 

.6 

1.2 

2.6 

2.6 1 

1.4 

1 .o ~ 

I 

1.1 j 

~ 

2.9 

3.5 

3.6 

3.5 

3.3 

3.4 

3.4 

3.4 

3.0 

3.3 

3.4 

3.6 

4.7 

4.9 

4.1 

4.4 

4.3 

' 4.3 

3.3 

4.2 

~ 4.1 

I 4.4 

! 

3.4 4.5 

3.1 : 3.8 

3.5 4.2 

3.6 ' 4.6 

3.5 5.1 

3.3 4.6 

3.3 3.9 

3.3 4.3 

3.5 4.7 

3.7 6.3 

3.7 I 6.3 

3.5 i I 5.0 

I 

2.1 

2.6 

1.6 

1.6 

2.1 

1.9 

1.8 

1.9 

2.0 

2.7 

2.0 

2.1 

1.8 

2.3 

1.8 

1.4 

1.2 

1.7 

2.2 

1.9 

1.4 

.6 

.5 

1.5 

Defensive 

AMIS 

3.9 

3.7 1 5.6 

3.8 I 5.6 

3.7 ' 5.6 

3.7 5.7 

5.2 3.7 

3.8 5.4 

3.9 6.5 

5.9 

3.9 

4.1 

4.0 

; 3.9 

! 3.9 

i 3.9 

; 3.7 

3.7 

i 3.7 

j 3.6 

I 3.7 

' 3.8 

; 3.7 

I 3.0 

2.9 

' 3.5 

6.0 

6.8 

5.9 

6.0 

5.7 

! 6.3 

5.5 

. 5.1 

4.9 

5.3 

5.9 

5.7 

5.1 

, 3.6 

3.5 

4.9 

1.11 I 3.39 I 4.49 I ~ 1.78 i 3.72 I 5.49 I 
STMUJATOR 

TARGET IMAGE 

GENERATION SYST 

(IN ADJACENT BUILDING) 

Figure 1.- Langley differential maneuvering simulator. 
SPHERE B 

(stick, rudder pc 

1 

I 

IMMANDS I 

- 

EQUATIONS OF MOT I ON 

FORCE MOMENT 

I 

I 1 I 1 AIRCRAFT. DATA 1 1 

I 1 I SCORI 

, I DRIVE I 

1 AIRCRAFT DATA I 

7 

EQUATIONS OF MOTLON I CONTROL COMMANDS * I 

(stick, rudder pedal, throttle) FORCE MOMENT 

SPHERE A .. I 1 L 
1 EQUATIONS OF MOTION , 

I 

ACM n,c 

LOG IC F * FORCE RATE LIMITS 

a FILTERS 

.c (PI (I, r) 

I 1 "1 I AI RCRAFT DATA A CL, CD, THRUST*-** 

I (performance only) 

. J 

IRELAT I VE 

GEOMETRY 

L 

AIRCRAFT DATA I 

CONTROL COMMANDS FORCE MOMENT 

I 

". . 

Figure 3.- IXYS-AML performance model. ,. 
STATE VARIABLES 

@C 6, w 

a 

a - 

F 

~ n,c 6 

EQUATIONS OF MOTION SYSTEM be - 

Ir CONTROL 

ACM 

LOG1 C 

A 

I 

I 

FORCE MOMENT C -t 

,I I 

A I RC RAFT DATA I 

CL, CD, Cmqt , - 

I/ c@  '** * 

- !L 

GEOMETRY * HARDWARE ~ A 

RELATIVE 

I DRIVE 1 

A I RCRAFT DATA 

CL, CD, Cmqt 

czB *****"' 

EQU AT I ON S OF MOT I ON 

(stick, rudder pedal, throttle) FORCE MOMENT 

CONTROL COMMANDS - - 

Figure 4.- DEIS-A@& control model. 
I 

i 

/ 

/ 

/ 

Figure 5.- Segmented flight path. 

59 
ADAPTIVE MANEUVERING LOGIC 

r- FROM EQUATIONS OF MOTION 

J RELAT I VE GEOMETRY-I- 

”” - ”- -” - - 

CURVE-FIT 

TR I AL MANEUVERS 

PRED ICT 

OPPONENT’S 

STATE AT 

t + 2 sec 

COMPUTE 

MANEUVER 

STATES AT 

COMPARE GEOMETRIC 

AN D STATE RELAT I ON S H I PS 

12 

1 

?COR !N G 

NUMERICAL VALUES 

SCO.RE 

I 

t 

.” . ” 

SPEC IAL MANEUVERS 

- ENERGY MANAGEMENT 

~~ 

- - - - - - - - - - 

GROUND AVO1 DANCE 

”””- - -+ ””””- -” 

EQUATIONS OF MOTION J 

~. . -~ - TO RELATIVE GEOMETRY 

Figure 6.- Adaptive maneuvering logic. 

60 
- PILOT C 

0 PILOT D 

0 

C 10 20 30 'IO 50 60 70 EO 90 100 110 120 130 190 1SC 16s 170 i83 

! .o 

U 

x 

5 ..5 

0 

0 10 20 30 'IO 50 60 70 80 90 100 110 120 130 1'40 150 160 170 180 

TIME s SECONDS 

Figwe 7.- Pilot-versus-pilot data set for run 1. 

PILOT C 0 PILOT F 

200 

L3 

w 

0 

x. 

0 

W 9 2. 

G 

z 

120 130 i40 150 160 170 180 

0 30 '43 50 60 70 80 90 100 110 120 133 i?tO is0 is0 iiC i80 

t 

0 

0 -0 

TIME . SECONDS 

Figure 9.- Pilot-versus-pilot data set for run 3. 
- PILOT C 

0 PILOT F 

wt 

Figure 10.- Pilot-versus-pilot data set for run 4. 
PILOT C 0 PILOT E 

W $ 2. 

a 

(L 

C 

.t 

0 

TIME , SECONDS 

Figure 11 .- Pilot-versus-pi-lot data set: for run 5. 
QI 

QI 

0 PILOT E 

- PILOT C 

13 

W 

0 

x. 

0 

0 

0 

0 [eo 

TIME v SECONDS 

Figure 12.- Pilot-versus-pilot data set for run 6. 
5 PILOT c 

PILOT F 

0 

T ItlE 9 SECONDS 

Figure 13.- Pilot-versus-pilot data set for run 7. 
PILOT F E PILOT 0 

0 10 20 30 90 50 60 70 80 90 100 110 120 130 190 1SO 160 170 180 

0 

c) 10 20 30 90 SO 60 70 60. 120y33 1Y-0 Illlll WIII Ill 1 llllll1 IIIIIIIII 

TIME 9 SECONOS 

Figwe 14.- Pilot-versus-pilot data set for run 8. 
PI LOT 0 PILOT 

F 

D 

r: 5.0 

Y 

(3 2.5 u 

a 

z 

01 

0 

60 70 BO 90 0 

0 10 20 30 90 50 60 70 80 90 100 110 120 130 140 150 i60 i70 180 

Figure 15.- Pilot-versus-pilot data set for run 9. 
- PILOT E 

PILOT D 

0 

Figure 16.- Pilot-versus-pilot data set for run 10. 
PILOT E E PILOT C 

wt 

0 

0 

0 10 20 30 YO 50 50 70 80 90 100 110 09 

IIIIIII 

T [ME v SECONDS 

Figure 17.- Pilot-versus-pilot data set for run 11. 
- PILOT . E . '-, . 

0 PILOT F 

.I 

200 

0 

W 

0 

x. 

0 

E 5.0 

10 22 30 90 50 60 70 80 90 100 110 120 130 190 15C I60 170 iB0 

0 I1 IIII 

i30 140 i5C. 160 1.70 180 

TIME 9 SECONDS 

Figure 18.- Pilot-versus-pilot data set for run 12. 
PILOT E 0 PILOT F .. 

200 

c3 

w 

0 

r< 

0 

Y 2.5 

z 

X 

0 

0 

0 10 2C 30 YO 50 60 70 80 30 100 110 120 130 190 150 160 170 190 

TIME s SECONDS 

Figure 19.- Pilot-wrsus-pilot data set for run 13. 
PILOT 0 E PILOT F 

I E 

0 

T IME SECONDS 
- PILOT . D , a . ." 

0 PILOT F 

.i . . .. ,. ". .. . .. 

200 E 

0 

0 

TIME v SECONDS 

Figure 21.- Pilot-versus-pilot data set for run 15. 
- PILOT 0 

0 PILOT C 

-I 

a0 

0 60 70 80 380 

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII, 

Om 

C 

0 10 20 30 YO 50 66G 

TIME s SECONDS 

Figure 22.- Pilot-versus pilot data set for run 16. 
- PILOT D 

0 PILOT C 

0 

w 

0 

4 

0 

II: 5.0 

Y 

W 

0 2.5 z 

E 

a 

0 

0 10 20 30 YO 50 60 70 80 90 100 110 120 130 1liO 150 i60 170 le0 

- 

C 

0 

TIME 9 SECONDS 

Figure 23.- Pilot-versus-pilot data set for run 17. 
J 

- PILOT F 

0 PILOT E 

0 10 20 30 LfO 50 60 70 80 50 100 110 120 130 140 150 160 170 180 

Figure 24.- Pilot-versus-pilot data set for run 18. 
PILOT 0 0 PILOT E 

0 

0 

0 

0 330 

T LYE 9 SECONOS 

Figure 25.- Pilot-versus-pilot data set for run 19. 

4 

W 
- PILOT D 

0 PILOT E 

c3 

W 

0 

.c 

0 

e 

t 

Figure 26.- Pilot-versus pilot data set for run 20. 
PILOT F 

PILOT C 

0 

3 

0 

TIME SECONDS 

Figure 27.- Pilot-versus-pilot data set for run 21. 
- PILOT E 

0 PILOT C 

c3 

w 

0 

4 

0 

2 .s 

0 

C 

10 2C 30 ‘40 50 60 70 80 30 100 110 i20 130 iY0 150 i60 170 is0 

0 

0 170 IIIIIIIIIII1IIIIIII eo * 100 110 120 13033 IIII IIIIIIIII IIIIIIIII II1111Ill 

TIME v SECONDS 

Figure 28 .- Pilot-versus-pilot data set for run 22. 
PILOT E 0 PILOT 0 

-e 

0 

TIflE * SECONDS 

Figure 29.- Pilot-versus-pilot data set for run 23. 

m 

W 
- PILOT F 

0 PILOT E 

L3 

W 

0 

x 

0 

c 

Figure 30.- Pilot-versus pilot data set for run 24. 
PILOT F E 9ML 

L3 

0 

W 

r< 

I 

0 

W 

a 2.5 

U 

z 

of 

C 

1SO 160 170 190 

10 

W 

0 

TIME rn SECONDS 

Figure 31 .- Pilot-versus-AbfL-control-model data set for run 1. 
- PILOT F 

[I] AML 

C 

0 

G ;

T IME m SECONDS 

Figure 32.- Pilot-versus-AML-cdntrol-model data set for run 2. 
0 RML 

- PILOT D 

fI 

Y 

W 

9 

I". 

IC 

J 

(I 

L 

G 

TIME, SECONDS 

Figure 33.- Pilot-versus-AML-control-del data set for run 3. 
- PILOT D 

0 AML 

200 

L3 

W 

0 100 

re 

C 

0 

0 

TIME , SECONDS 

! 

Figure 34.- Pilot-versus-AML-control-model data set for run 4. 
- PILOT F 

0 AML .& 

3 

3 

TI ME v SECONDS 

Figure 35.- Pilot-versus-AML-control-model data set for run 5. 
PILOT F 0 FlnL 

13 

W 

0 

. IO0 

U 

r 

0 

0 10 20 30 YO 50 60 70 80 90 100 110 120 130 140 i53 160 i73 i90 

ti 

TIME e SECONDS 

Figure 36.- Pilot-versus-AML-control-model data set for run 6. 
PILOT C 0 AML 

0 

0 

0 

TIME. SECONDS 

Figure 37.- Pilot-versus."control-model data set for run 7. 
- PILOT C 

0 RML 

200 

I3 

W 

0 

100 

x 

C 

c 

P 

.J 

Figure 38.- Pilot-versus-AML-control-model data set for run 8. 
0 AML - PILOT D 

c3 

w 

0 

IOC 

4 

C 

0 

0 

0 10 

TIME , SECONDS 

Figure 39.- Pilot-versus-AML-control-model data set for run 9. 
- PILOT I-1 

RIIL 

W 

L3 

O 

x 

0 

0 

t 

Y 

W 

3 

c 

ICE 

1 

CI 

1°EL 

0 

0 L*3 

TIME, SECONDS 

Figwe 40.- Pilot-versus-AML-control-@el data set for run 10. 
3 

r 

0 

0 2s 

TIME, SECONDS 

Figure 41 .- Pilot-versus-AML-control-model data set for run 11. 
W 

QI 

- PILOT C 

0 RML 

200 

t3 

W 

0 

LOG 

4 

I 

C 

0 

Figure 42.- Pilot-versus-AML-control-model data set for run 12. 

‘L 
PILOT c ii AML 

i 

0 

3 

0 

T J ME v SECONDS 

Figure 43.- Pflot-versus-A”control-mode1 data set for run 13. 
c3 

W 

0 

x 

0 

.J 

G 

0 2s 

TIME v SECONDS 

Figure 44.- Pilot-versus-AML-control-del data set for run 14. 
200 

c3 

W 

0 

'1 00 

x 

C c 

D 

3 

L 

G 

TIME v SECONDS 

Figure 45.- PiJot-versus-AML-control-model data set for run 15. 
Y 

0 

0 

- PILOT E 

0 AML 

L3 

W 

0 

x 

3 

1 

Figure 46.- Pilot-versus-AML-control-model data set for run 16. 
- PILOT D 

0 AML .. 

0 

3 

TIME s SECONDS 

Figure 47.- Pilot-versus-AML-control-model data set for run 17. 
- PILOT F 

0 RML 

0 

3 

TIME , SECONDS 

Figure 48.- Pilot-versus-AML-control-model data set for run 18. 
PILOT E 5 AML 

L3 

W 

0 

x 

0 

3 

Figure 49.- Pilot-versus-AML-control-del data set for run 19. 
4 

0 

8P 

I 

i 

! 

- PILOT E 

0 AML ..' I 

(3 

W 

0 

x 

0 

3 

TIME s SECONDS 

Figure 50.- Pilot-versus-AML-control-model data set for run 20. 
PILOT D 0 AHL 

c3 

W 

0 

4 

0 

0 

T JME , SECONDS 

Figure 51 .- P~lot-versus-~-control-model data set for run 21 
0 

1 

0 

T IME SECONDS 

lrigure 52.- Pilot-versus~ML"trol"1 data set for IUR 22. 
3 

T I ME s SECONDS 

Figure 53.- Pilot-Versus-AHL-control-mdodcl data set for run 23. 
- PILOT F. 

0 AML 

200 € 

e3 

W 

0 LOO 

4 

0 

0 

TIME SECONDS 

Figure 54.- Pilot-versus4UlL-control-model data set for run 24. 
PILOt D 0 RML 

~ 

0 

x 

0 

TIME s SECONDS 

Figure 55.- Pilot-versus-ma.,-performance-niodel data set for run 1. 
- PILOT 0 

0 AML 

Figure 56.- Pilot-versus-AML-performance-model data set for run 2. 
PILOT C 0 Am 

(3 

W 

0 

x 

0 

TIME s SECONDS 

Figure 57.- Pilot-versus-AML-performancemodel data set for run 3. 
- PILOT C 

0 AML 

u 

W 

O 

x 

0 

0 

1 TO 

0 

0 10 20 30 YO 50 60 70 10 

TIME. SECONDS 

IIIIII Ill 

Figure 58.- Pilot-versus-AML-performance-model data set for run 4. 
0 

Figure 59.- Pilot-versus-AHL-performance-model data set for run 5. 
- PILOT C 

El AML 

TIME , SECONDS 

Figure 60.- Pilot-versus-AML-performance-model data set for run 6. 
200 

I3 

W 

0 

too 

4 

r 

c 

0 

TIME SECONDS 

Figure 61.- Pilot-versus-AML-performance-model data set for run 7. 
PILOT E 6 AML 

a 

W 

0 

x 

0 

W 

LL: 

U 

9 2.5 

0 

0 

-t 

0 

0 

TIME , SECONOS 

Figure 62.- Pilot-versus-AML-performance-model data set for run 8. 
PILOT E E flML 

a 

W 

0 

4 

0 

0 

0 

0 180 

TIME . SECONDS 

Figure 63.- Pilot-versus-AML-performance-model data set for run 9. 
- PILOT 0 

0 AML 

0 

0 

T IME , SECONDS 

Figure 64.- Pilot-versus-Ab&-performance-model data set for run 10. 
PILOT 0 E FlML 

L9 

W 

0 

x 

0 

120 130 190 150 160 170 180 

0 

0 10 

TIME,. SECONDS 

Figure 65.- Pflot-versus-AML-performance-model data set for run 11. 
- PILOT F 

0 RML 

ld 

W 

U 

1 O'J 

a 

I 

C 

Figure 66.- Pilot-versus-AML-performancede1 data set for run 12. 
- PILOT F 

0 AML 

c3 

W 

0 IO0 

a 

C 0 

0 

0 

0 

0 [

TINE s SECONDS 

Figure 67.- Pilot-versus-AML-performancemodel data set for run 13. 
- PILOT F 

0 AML 

W 

0 

r< 

0 

0 

W 

0 

0 

TIME SECONDS 

Figure 68.- Pilot-versus-AFbperformance-mOdel data set for run 14. 

I 
200 Ecl 

0 

W 

4 

0 

0 10 20 30 '40 50 60 70 EO 90 100 110 120 130 190 150 160 173 180 
- PILOT E 

0 RML 

W 

c3 

0 

x 

0 

W 

cl 2. z 

U 

CY 

0 10 20 30 110 50 60 ~ 70 . EO 90 100 710 120 130 1110 150 160 170 180 

0 

rt 

TIME . SECONDS 

Figure 70.- Pilot-versus-AML-performance-model data set for run 16. 
0 RML 

- PILOT D 

w 

LI) 

0 

x 

0 

0 

0 

TIME s SECONDS 

Figure .71.- Pilot-versus-AML-performance-model data set for run 17. 
- PILOT D 

0 RML 

c3 

W 

O 

x 

0 

TIME , SECONDS 

Figure 72.- Pilot-versus-AMGperformance-model data set for run 18. 
- PILOT F 

0 AML 

IF 

4 

TIME , SECONDS 

Figure 73.- Pilot-versus-AML-performancemodel data set for run 19. 
- PILOT E 

0 RML 

200 

u 

W 

0 100 

rc 

c 

0 

TIME e SECONDS 

Figure 74.- Pilot-versus-AMGperformance-model data set for run 20. 
0 FlML 

- PILOT E 

L9 

W 

0 

4 

3 

0 

T IHE s SECONDS 

Figure 75.- Pilot-versus-AML-performance-model data set for run 21. 
d 

W 

0 

PILOT E 0 RML 

I 

0 

TIME s SECONDS 

Figure 76.- Pilot-versus-AML-performancemodel data set for run 22. 
PNCT F 0 RML 

3 

3 

5 

G 31 

TIME * SECONDS 

Figure 77.- Pilat-versus-AML-performance-model data set for run 23. 
d 

w 

N 

PILOT F E RtlL 

3 

0 10 20 30 90 50 60 70 80 30 100 110 120 130 190 IS0 169 170 183 

Figure 78.- Pilot-versus-AML-performance-model data set for run 24. 
90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

- 

E 

"""" 

Mean 

Pilots 

L 

Figure 79.- Capatirroru of time on offense with advantage' for 

pilots versus pilots. 

133 
d 

W 

L 

80 

70 

60 

50 

40 

30 

20 

10 

0 

AML performance model 

"" """"""""_ """" AML 

""_ 

, -. 

mean 

mean 

Figure 80.- Time on offense with advantage for individual pilots versus .BML performance eel. 
n Pilot 

AML control. model 

80 

"" "" 

Figure 81.- Time on offense with advantage for individual pilots versus AML control model. 
.. 

”””” 

Mean 

c 

Pilots 

Figure 82.- Canpatisons of AML score8 for pilots versus pilots. 

1 36 

.. 

~ 
.m AML performance model 

I' . .. ., , . 

1 ,. . . " 

- """"" 

AML mean 

Figure 83.- AML score of individual pilots versus AML performance model. 
rfl AML control model 

Figure 84.-.AML score of individual pilots versus AML control model. 
60 

50 

10 

0 

C 

- 

IJ Pilot 

AML control model 

AML performance model 

Figure 85.- Average time of first entry into 2one.A for individual 

pilots and AML against common opponent. 

139 
60 

50 

u 40 

c- 20 

10 

0 

30 

(u 

.- Er 

1.. 

Pilot 

r? AML control model 

AML performance model 

- 

F 

Figure 86.- Average time in zone A for individual pilots and 

AML against common opponent. 

140 
120 

100 

110 - 

f- 

- 

90 - 

80 - 

70 

60 

- 

- 

c 50- 

40- 

30 - 

20 - 

10 - 

0 --- 

8 

v) 

z- .- 

C 

- 

D 

I] Pilot 

AML control model 

AML performance model 

- 

F 

Figure 87.- Average time of first entry into zone B for individual 

pilots and AML against common opponent. 

141 

I 
Pilot 

rA AML controi model 

AML performance model 

40 

30 

20 

PO 

0 

Figure 88.- Average time in zone B for individual pilots and.BML 

against common opponent. 

142 
u 

a 

v) 

a- 

€ .- 

ID 

rA AML control model 

n 

Figure 89.-.Average time of first entry into zone A for individual 

pilots versus. AML control model. 

143 
I 

u 

Q) 

v) 

.I $ 

Ip7 AML control model 

50 

40 

30 

20 

10 

0 

Figure 90.- Average time in zone A for individual pilots Versus 

AML control model. 

144 
100 

80 

u 60 a 

v) 

30 t zoN 10 0 !

- 

D 

AM1 control model 

Figure 91 .- Average time of first entry into zone B for individual 

pilots versus. AML control model. 

1 45 
AML control model 

Figure 92.- Average time in zone B for individual pilots versus 

AML control model. 

1 46 
100 

90 

80 

70 

u 60 

Q) 

v) 

50 

c40 

.- E30 

20 

10 

0 

11 Pilot 

a AML performance model 

Figure 93.- Average time of first entry into zone A for individual 

pilots versus AML performance model. 

1 47 
AML performance model 

Figure 94.- Average time in zone A for individual pilots versus 

'1 AML performance del. 

1 48 
110 

100 

90 

80 

70 

.60 

.- g 50 I"+ 

40 

30 

20 

10 

0 

AML performance model 

F 

Figure 95.- Average time of first entry into zone B for individual 

pilots versus.AML performance model. 

149 
AML performance model 

atFigure 96.- Average time in zone B for individual pilots versus 

AML performance model. 

I3 U.S. GOVERNMENT PRINTING OFFICE: 1979-635-004/24 

1 50 
"" ". - - -- " . . .-. ~ .~ ~~~ "_ 

- ~~. - - I -. - I - J 2. Government Accession No. 3. Recipient's Catalog No. 

NASA TP-1518 

COMPUTER-AUTOMATED OPPONENT FOR MANNED AIR-TO-AIR ~. J "" . __ - 

COMBAT SIMULATIONS 6. Performing Organization Code 

Walter W. Hankins I11 

~ 10. Work Unit No. 

I 9. Performing Organization Name Address I 723-01-03-03 

I NASA Langley Research Center 

Hampton, VA 23665 I

. " 

11. Contract or Grant No. 1 . 

__ 13. Type of Report and Period Covered 

National Aeronautics and Space Administration 

Washington, DC 20546 14. Sponsoring Agency Code 

. - "- - ". " -___ 

15. Supplementary Notes 

Appendix A by Bobby J. Glover, Kentron International, Inc. 

16. Abstract 

~ - ~~ - ~~ ~- "" - "_ - 

Two versions of a real-time digital-computer program that operates a fighter 

airplane interactively against a human pilot in simulated air combat have been 

evaluated. They function by replacing one of two pilots in the Langley differential maneuvering simulator. Both versions make maneuvering decisions from 

identical information and logic; they differ essentiallyn the aerodynamic 

models that they control. One is very complete, but the other is much simpler

primarily characterizing the airplane's performance (lift, drag, and thrust). 

Both models competed extremely well against highly trained U.S. fighter pilots. 

- . .. . . - " - 

18. Distribution Statement 

.. 

Simulation Unclassified - Unlimited 

Air combat 

Differential games 

Control systems 

Artificial intelligence -. .- - " ." - ". . 

19. Security Classif. (of this report) 20. Security Classif. (of this page) I . . - .. _" . - ~~ - ." 

Unclassified Unclassified $7.25 ~. .~ . . . . ". . ." . .. -" 

Subject Category 59 

* For sale by the National Technical Information Service, Springfield, Virginia 22161 
National Aeronautics and 

Space Administration 

Washington, D.C. 

20546 

Official Business 

Penalty for Private Use, $300 

THIRD-CLASS BULK RATE Postage and Fees Paid 

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