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NASA Technical Memorandum 107722 


TRIAL MANEUVER GENERATION AND 
SELECTION IN THE PALADIN TACTICAL 
DECISION GENERATION SYSTEM 


ALAN R. CHAPPELL 
JOHN W. MCMNAUS 
KENNETH H. GOODRICH 


JANUARY 1993 


{ NAS A-TM-10 7722) Trial MANEUVER 

GENERATION AND SELECTION IN THE 
PALADIN TACTICAL DECISION 
GENERATION SYSTEM (NASA) 13 p 


NASA 

National Aeronautics and 
Space Administration 


Langley Research Center 

Hampton, Virginia 23681-0001 


H2/08 


N93-23088 
Unci as 
0151384 



ERRATA 


NASA Technical Memorandum 107722 

TRIAL MANEUVER GENERATION AND SELECTION IN THE PALADIN 
TACTICAL DECISION GENERATION SYSTEM 

Alan R. Chappell 
John W. McManus 
Kenneth H. Goodrich 

January 1993 


The title should be changed from Trail Maneuver... to Trial Maneuver... 
A revised cover and Report Documentation Page are attached. 


Issued April 1993 



TRIAL MANEUVER GENERATION AND SELECTION IN THE PALADIN 
TACTICAL DECISION GENERATION SYSTEM 


Alan R. Chappell* 

Lockheed Engineering and Sciences Company 
Hampton, Virginia 


John W. McManus** and Kenneth H. Goodrich t 
NASA Langley Research Center 
Hampton, Virginia 


Abstract 

To date, increased levels of maneuverability and 
controllability in aircraft have been postulated as tactically 
advantageous, but little research has studied maneuvers or 
tactics that make use of these capabilities. In order to help 
fill this void, a real-time tactical decision generation system 
for air combat engagements. Paladin, has been developed. 
Paladin models an air combat engagement as a series of 
discrete decisions. A detailed description of Paladin's 
decision making process is presented. This includes the 
sources of data used, methods of generating reasonable 
maneuvers for the Paladin aircraft, and selection criteria for 
choosing the "best" maneuver. Simulation results are 
presented that show Paladin to be relatively insensitive to 
errors introduced into the decision process by estimation of 
future positional and geometric data. 

Introduct ion 

Modern air combat simulations must perform in a 
greatly expanded and rapidly changing tactical environment. 
Such a simulation system must be able to model new 
aircraft and their advanced capabilities. The system should 
have a modular software structure so that new weapons 
systems or aircraft subsystems (e.g. sensors or propulsion 
systems), modifications to aircraft control systems, or 
changes to the aircraft configuration can be easily 
incorporated. In support of the study of superagile aircraft at 
the Langley Research Center (LaRC), a Tactical Guidance 
Research and Evaluation System (TiGRES) is being 
developed. The design and development of TiGRES as well 
as its relationship to past and current air combat simulation 
systems is described in detail in reference 1. 

The TiGRES system is designed to allow researchers to 
develop and evaluate aircraft systems in a tactical 
environment. The three main components of TiGRES are a 
Tactical Decision Generator (TDG), the Tactical Maneuver 


* Senior Engineer, P.E., Member AIAA. 

** Aerospace Engineer, 
t Research Engineer, Member AIAA. 


Simulator (TMS), and the Differential Maneuvering 
Simulator (DMS). 

A TDG is an intelligent system that selects the combat 
maneuvers to perform throughout an air combat 
engagement Both the TMS and the DMS use a TDG as the 
automated opponent. Paladin is the TDG currently used in 
TiGRES research. 

The TMS 2 provides a high-fidelity batch air combat 
simulation environment for the development and testing of 
various guidance and control strategies. The researcher 
defines the initial conditions of the air combat engagement 
and the TMS then controls the aircraft using either simple 
trajectory commands or a tactical decision generation 
system. The main elements of the TMS are a high-fidelity, 
nonlinear six degree-of-freedom (d.o.f.) rigid-body aircraft 
dynamic model, including the control system, a TDG, and a 
user interface. 

The DMS consists of two 40' diameter domes and one 
20' diameter dome. The facility is intended for the real-time 
simulation of air combat engagements between piloted 
aircraft. By using a TDG to control one of the airplanes, it 
is possible to test a TDG against a human opponent. This 
feature allows the guidance logic to be evaluated against one 
or more unpredictable and adaptive human opponents. 

The Paladin System 

Paladin is a knowledge-based TDG. Knowledge-based 
systems use a large amount of information about a 
problem's domain to understand and solve that problem. 
Paladin was implemented using a large amount of 
information about aircraft, flight control, and air combat so 
that the system could provide insight into both the tactical 
benefits and the costs of enhanced maneuverability. 

Paladin uses an object-oriented programming approach 3 
to represent each aircraft in the simulation. Each aircraft 
object includes information on the current state of the 
aircraft’s offensive systems (e. g. guns, missile systems, 
fire control radars, etc.), defensive systems (e. g. electronic 


1 



counter-measures, chaff, etc.), and propulsion system. This 
state information is used to help guide Paladin's reasoning 
process. 

Paladin utilizes modular software subroutines and 
specialized computer hardware. The separation of the aircraft 
simulation and decision logic components allows each 
module or knowledge source to be designed and implemented 
using the hardware and programming techniques specifically 
suited for its function. The use of highly specialized and 
independent knowledge sources also provides for modular 
protection 3 , confining the effect of an error occurring in a 
module at run-time to that module, or to a small set of 
neighboring modules in the program. The confining effect 
of the modular protection was used to aid in the design and 
debugging process of Paladin. Each knowledge source was 
developed and tested independently before it was incorporated 
into the system. 

The independence of the knowledge sources also 
increases the efficiency of Paladin by allowing knowledge 
sources to be distributed across a network of several 
heterogeneous processors. The network currently consists 
of a Symbolics 3650^ workstation, a Symbolics Maclvory 6 
workstation, and four Vax 3200° class workstations. 
Communication between the distributed knowledge sources 
is achieved using customized DECNet-based Client/Server 
software developed in-house for TiGRES. This software 
allows for synchronization, communications, and data 
sharing between heterogeneous computers running the 
DECNet communications protocol. Paladin is implemented 
as a serial blackboard system 4 , so no serialization or 
concurrency related software is required. Each knowledge 
source requests all of the data required to perform its 
computation from the blackboard at the start of its execution 
cycle, and posts its results to the blackboard at the end of its 
execution cycle. 

The development of Paladin has been a multi-stage 
process using a baseline version of the Adaptive 
Maneuvering Logic 5 (AML) program as the starting point. 
Figure 1 is a schematic of Paladin. As in AML, Paladin 
models a combat engagement as a series of discrete 
decisions. Hence, at temporally regular decision points, the 
system must choose the "best" tactical maneuver to follow 
until the next decision point. To make this choice, Paladin 


t Symbolics 3650 is a registered trademark of Symbolics 
Incaporated. 

* Maclvory is a registered trademark of Symbolics 
Incoporated. 

D Vax 3200 & DECNet are registered trademarks of Digital 
Equipment Corporation. 


uses information about its own state and estimated data 
about the opponent to calculate the relative geometry 
between the two aircraft This relative geometry is used to 
perform a situation assessment and to choose a new throttle 
position. After extrapolating the opponent's state a short 
time into the future, Paladin generates a situationally 
dependent set of trial maneuvers. A future engagement state 
is predicted for each of the trial maneuvers. These future 
engagement states are passed through a group of scoring 
functions that evaluate various aspects of the tactical 
situation. The results of the scoring functions are weighted, 
based on the mode of operation, to compute the current best 
maneuver. This maneuver is then used to direct the aircraft 
until the next decision interval. In the following sections, 
each of these steps will be described in detail. 

Estimation of Opponent Information 

To begin each decision cycle. Paladin must obtain 
certain information that will be needed throughout the 
decision making process. Data about the ownship (Paladin's 
aircraft) is readily available. However, information about 
the opponent would be less available outside the simulation 
environment (in a real aircraft). Paladin, thus, receives only 
the opponent’s absolute X, Y, and Z coordinates and infers 
all other needed data. No noise is added to this position 
input. Previous research 6 has shown that realistic noise on 
the input has negligible impact on Paladin's capabilities, 
while it impairs repeatability and causal assessment. 

Using the three most recent positions for the opponent, 
a quadratic curve fit is made for the flight path. From this 
flight path, a velocity and load factor are estimated. 
Assuming that the opponent's aircraft is aerodynamically 
similar to Paladin, all other needed data are estimated. 
Using these estimations and Paladin's known data, relative 
geometry parameters are calculated for use by the Situation 
Assessment Module and the Active Throttle Controller. 

Situation Assessment Module 

Six modes of operation, shown in table 1, have been 
incorporated in Paladin. The Situation Assessment 
knowledge source is used to model a pilot’s situational 
awareness and changing problem-solving strategies. Just as 
a pilot will recognize the difference between an aggressive 
and an evasive situation and react accordingly, the Situation 
Assessment knowledge source provides information 
allowing Paladin to adapt its problem-solving strategy 
based on the current situation. The determination of the 
current mode of operation is based on the aircraft's current 
mission, the current state of the aircraft’s systems, and the 
relative geometry between the aircraft and its opponent. 
Each of the six modes of operations has a unique vector of 
scoring weights and a unique decision interval (shown in 
table 1). The scoring weights 6 for each mode of operation 


2 




Figure 1. Schematic of The Paladin System 


have been adjusted during the design and testing process to 
maximize Paladin's performance in that mode of operation. 
The testing procedures used to evaluate Paladin’s 
performance are described in detail later in this paper. The 
Situation Assessment used by Paladin is covered more 
completely in reference 7. 

Active Throttle Controller 

A rule-based Active Throttle Controller was developed 
to determine throttle and speed brake settings based on the 
engagement situation. The throttle controller can set the 
throttle to any position between idle and full afterburner, and 
the speed brake to any position between fully retracted and 


T^bleLJNfodesofOgeration 


Mode 

Decision Interval 

Aggressive 

0.25 sec 

Defensive 

0.5 sec 

Evasive 

0.25 sec 

Ground Avoidance 

0.125 sec 

Neutral 

1.0 sec 

Disengage 

0.5 sec 


fully extended. The throttle controller uses the current mode 
of operation and the relative geometry information to select 
one of four operational modes. These four modes are 
maintain best cornering speed mode, maintain/set range 
mode, separate quickly mode, and force overshoot mode. 
Each mode has a set of specific throttle control rules that are 
used to maximize system performance in that throttle mode. 
The throttle position produced is considered a suggested 
throttle position, as maneuver generation can accept this 
position or select another in order to coordinate the throttle 
with the maneuver. The Active Throttle Controller is 
covered in detail by reference 7. 

Extrapolation of Opponent's Near Future 

Prior to deciding what maneuver to execute. Paladin 
estimates where the opponent will be at the end of the look- 
ahead period, assuming no change in the opponent's current 
maneuver. Currently, a look-ahead period of 4 times the 
decision cycle is used to enhance system performance^. 
This estimation of future state is necessary to guide the trial 
maneuver generation process and for selecting the best 
maneuver. Using the quadratic curve fit for the flight path 
of the opponent established by the estimation process 


3 














(discussed earlier), a position, velocity, and load factor are 
extrapolated. Again assuming the opponent's aircraft is 
aerodynamically equivalent to Paladin's, all remaining data 
about the state of the opponent's aircraft is then calculated. 
This information will be used to calculate a future relative 
geometry between the aircraft for use in scoring Paladin's 
trial maneuvers. 

However, the opponent can and does change his current 
maneuver. Extrapolation into the future, therefore, 
introduces a certain amount of error into the ensuing 
calculations. The magnitude of these errors and their effects 
on Paladin's capabilities are discussed later in this paper. 

Trial Maneuver Generation Module 

At the end of each decision interval Paladin must 
choose a maneuver to execute for the duration of the next 
decision interval. This is accomplished by generating a set 
of trial maneuvers, predicting the engagement state to some 
point in the future based on each of these trial maneuvers, 
scoring the future engagement states, and finally, selecting 
the maneuver with the highest score. 

Trial maneuver generation uses several maneuvering 
concepts throughout the generation process that bear 
advanced definition. The maneuver plane is the plane 
containing the current velocity vector and the net force 
vector on the aircraft. Hence, this is the plane which will 
contain the flight path for some limited period of time. 
The bank to intercept 3 is the rotation angle around the 
velocity vector that places the opponent in the maneuver 
plane and above the maneuvering aircraft. The optimum 
cornering speed is the speed at which the aircraft has its 
fastest sustained turn rate. 

Paladin generates a set of trial maneuvers completely 
dependent upon the current engagement situation. Using 
the situational information. Paladin selects one of five 
maneuver generation schemes. These schemes are fine 
tracking, over-the-top reversal, high-speed turning, low- 
speed turning, and target acquisition. 

Fine Tr a c king 

Fine tracking maneuvers are used when Paladin is close 
to, or has, a weapons solution. These maneuvers are 
clustered tightly around the bank to intercept and use a 
small fraction of the available maximum load factor. 
However, as range decreases, it may be necessary to turn 
sharply to track an active target. Therefore, one group of 
maximum load factor maneuvers is generated. The trial 
maneuvers generated in fine tracking are the 28 
combinations of intercept bank angle and intercept bank 
angle ±2.5% ±5.0% ±7.5" at loads of 10%, 20%, 40%, and 
100% of the maximum available load. 


Over-the-Top Reversal 

Over-the-top reversal maneuvers are used in a limited 
number of situations. If Paladin's velocity is above the 
current calculated comer velocity and either, the two aircraft 
are headed in nearly opposite directions, or the opponent is 
following nearly directly behind Paladin, then the over-the- 
top reversal maneuvers are generated. Only one maneuver 
is generated in these situations, a maximum load factor pull 
up with a bank angle of 180.0° minus the current azimuth 
angle. Given the situations in which this maneuver is 
generated, the result is a near vertical pull up maintained as 
long as the conditions stated above hold. Acquisition 
maneuvers will then take over to allow Paladin to roll out 
of the loop and pursue the opponent. 

High-Speed Turning 

High-speed turning maneuvers are used in cases similar 
to those of the over-the-top reversal, allowing larger 
directional differences between the aircraft. In these cases, 
where Paladin has a long way to turn and is above comer 
velocity, a high oblique turn is the best choice for several 
reasons. Primarily, the vertical component of the turn 
drops Paladin's velocity to near comer much faster than a 
flat turn (resulting in a speed through the turn which is 
closer to the optimum cornering speed). As an added 
benefit, the excess speed is traded for altitude, which can be 
used later to recover velocity lost in a turning contest. 
High-speed turning maneuvers are generated as maximum 
load factor turns between 0° and 45’ off vertical (in 5° 
increments). Only turns in the direction of the opponent 
are generated, limiting these situations to ten trial 
maneuvers. 

Low-Speed Turning 

Low-speed turning maneuvers are used when Paladin 
has a long way to turn and is below comer velocity. These 
maneuvers force a diving turn where the vertical component 
of the turn raises/maintains Paladin's velocity nearer to 
comer than would a flat turn (resulting in a speed through 
the turn which is closer to the optimum cornering speed). 
Low-speed turning maneuvers are generated as maximum 
available load factor turns at bank angles between 135° and 
180° (in 5° increments). Only turns in the direction of the 
opponent are generated, limiting these situations to ten trial 
maneuvers. 

Target Acquisition 

In the largest part of typical engagements none of the 
limited situations described for the generation schemes 
above holds. When this happens, target acquisition 
maneuvers are generated. Since target acquisition 
maneuvers are used in widely varying engagement states, 
some restrictions on the generation have been developed to 
ensure that the trial maneuvers do not violate the Paladin 


4 



maneuvering assumptions and are physically realistic. The 
development of these limits will now be described. 

In order to choose a maneuver for the next decision 
interval Paladin evaluates the position achieved by the trial 
maneuvers with respect to the extrapolated position of the 
opponent. This choice will be impaired by errors in the 
positions being evaluated. Moreover, maneuvers that 
consistently produce overly optimistic or pessimistic 
results will tend to bias the selection process away from the 
best results. It is, therefore, necessary to restrict maneuvers 
to those which produce flight paths that do not violate the 
basic assumptions of the prediction algorithm and are inside 
the aircraft's maneuvering capabilities. 

Maintainable Limits 

The prediction algorithm used by Paladin assumes that 
the flight path will be in a selected maneuver plane with a 
net positive load factor. These assumptions are met by 
only generating maneuvers which balance all out of the 
maneuver plane forces and yield a net positive vertical force 
in the maneuver plane. These limits on maneuvers are 
called the maintainability limits. (Since the limits 
represent assumptions made by the prediction algorithm and 
the prediction algorithm ignores the affect of thrust, the 
equations presented below do not include the thrust 
component.) 

A balance of out of the maneuver plane forces can be 
stated as follows: 

W cos 0 sin p + L sin <t>* = 0 , (la) 


where W is the aircraft weight, L is the aircraft lift, 0 is the 



pitch angle of the maneuver plane, p is the rotation angle of 
the maneuver plane, and <&* is the offset between p and the 
wind axis bank angle (<&). Figure 2 gives a graphic 
representation of these angles. Solving for O* yields. 


<& = arc sin I - 


cos 0 sin p 


\ 


(lb) 


where n is the load factor, or L / W. For this relation to 
hojd, the magnitude of the argument to the arcsine function 
must be less than or equal to one. Hence, 


cos 0 sin p 

-1 <- < 1 . 


(lc) 


Noting that cos 0 = Vjj / V, horizontal velocity over total 
velocity, and that this quantity is always positive, 


n V 

V H 


< sin p < 


n V 

V H ’ 


(Id) 


which yields boundaries at angles of ± arcsin (n V / V H ) and 
180° - [± arcsin (n V / V H )]. As would be expected, these 
limiting equations indicate that the assumption stated in 
equation la will always be met using load levels greater 
than one gravity (g). The limits establish two regions, 
symmetric around maneuver planes of ±90°, where this 
assumption will not be met with load levels below one g. 
These regions are shaded in medium grey in figure 3. 

A positive net vertical force in the maneuver plane can 
be stated as follows: 


L cos d>* - W cos 0 cos p > 0 . 


(2a) 


This reduces to 

cos p < n V cos 4>* / V H (2b; 

which establishes a symmetric region around the 0° 
maneuver plane with limits at ± arccos (n V cos «t>* / Vjj) 
where relation 2a will not be satisfied. This region is 
shaded in light grey in figure 3. 

Achievable Limits 

Likewise, a constraint is placed on the bank angles that 
can be physically achieved by the Paladin aircraft. 
Modeling the current wind axis roll rate as a limited, first- 


5 




Maneuver plane rotation = O' 



Bank to 


Unachievable 
bank angles. 
Area defined by 
equ. 5d and 6g. 


Trial banks are intercept bank and 
intercept bank ± 5% 10% and 15% 

180' 


Velocity vector normal to 
and into the paper. 


Unable to maintain 
maneuver at these bank 
angles. 

Areas defined by equ. Id. 


Unable to pull up in 
maneuver plane at these 
bank angles. 

Area defined by equ. 2b 


Figure 3. Trial Bank Elimination at a Given Load Level 


order lag response, the maximum roll rate in one direction 
at any time in the future can be expressed as follows: 

d 

= (^max ‘ ®DX1 - e x ) + <&o (3) 

where O max is the maximum roll rate (+ or - depending on 

direction of roll to be limited), Oq is the current roll rate, t 

is the elapsed time from now, and z is the lag time 

constant. <b max and t are interpolated from data tables 

based on commanded angle of attack (a). These data tables 
represent the approximate response of the 6 d.o.f. aircraft 
model. Figures 4 (baseline aircraft) and 5 (thrust vectored 


aircraft) show the < I > max (cx) and x(a) relationships. O max 
is further limited based on airspeed such that, 

O max < 0.9375 V (4) 

for speeds less than 160 ft/sec. 

Integrating <t>(t) over the prediction interval yields the 

maximum possible change in roll during this time period. 
Hence, 


6 



1.2 



0 10 20 30 40 50 60 
Alpha (deg) 

—a— Maximum Roll Rate (deg/sec) 

• — Time Constant (sec) 

Figure 4. d> max (a) and x(a) for the Baseline Aircraft 



0.9 


0.6 


0.3 


o 

o 

05 


a 

8 

VS 

e 

o 

U 

o 

B 

H 


0.0 


0 10 20 30 40 50 60 
Alpha (deg) 

— Et- Maximum Roll Rate (deg/sec) 
— • — Time Constant (sec) 


Figure 5. <& max (a) and x(a) for the Thrust Vectored 
Aircraft 


A roll = 
At 
r - 


-t 


K^max - W - e** ) + Oq] dt (5a) 


0 


A roll = 
At 


l* 


<®max - *0) dt - 
At 


-t 


At 


(<&max - > d t + *0 dt (5b) 


J* 


0 

A roll = 

(d>max - d>o) A t - 


-At 


(°max - <5 o)(-x)(e x - e°) + O 0 At (5c) 


-At 


A roll = «b max At + x(d> 

max ’ * 0 )(et -1) (5d) 


where At is the length of the prediction interval. 

Therefore, we have limits on how far the aircraft can 
physically roll in the wind axis system. In order to 
establish the limits on the achievable maneuver planes, a 
rotation angle p must be found that corresponds to each of 


these physical limits. Restating equation la, including a 
term for thrust (T), 

W cos 0 sin p + (L + T sin a) sin d>* = 0 . (6a) 


Solving for sin O*, and substituting d> - p for d>* , yields: 


sin (<t> - p) = 


^ W cos 0 sin p^ 


V L + T sin a J ' 

A formula for p can then be derived as follows: 


sin 0 cos p - cos O sin p = 
sin <b cos p 


sin p 


cos <t> 


f W cos 0 sin p^ 
v L + T sin a , 

W cos 0 A 


V L + T sin ay 


W cos 0 


cos p _ cos Q _ 
sin p - sin d> (L + T sin a) sin <J> 


(6b) 


(6c) 


(6d) 


(6e) 


sin p (L + T sin a) sin d> 


cos p + T sin a) cos - W cos 0 


p = arctan 


(L + T sin a) sin d> 


(L + T sin a) cos <b - W cos 0 


(60 


(6g) 


7 





Using the maximum wind axis roll in both directions as 
calculated in the previous paragraph, maneuver plane 
rotations can be found which together define an area that is 
unachievable. This area is shaded in dark grey in figure 3 
and is called the unachievable area. 

In order to implement these restrictions on trial 
maneuver bank angle, a set of trial maneuvers that is 
appropriate to the situation is identified for testing. This 
set of trial maneuvers is defined by the intercept bank angle 
and intercept bank angle ±5% ±10', and ±15' at loads of 
100%, 80%, 60%, 40%, 20%, and 1% of the current 
maximum load factor (42 possible combinations). A 
predefined maximum number of bins are available for 
storing trial maneuvers. A combination of load factor and 
bank angle is selected from the above lists as a candidate 
trial maneuver. If the trial maneuver falls in any of the 
unusable areas described above, it is discarded. Otherwise, 
the trial maneuver is placed in one of the bins to be sent to 
the scoring routine. Once all of the bins are filled, or all 
combinations are tested, trial maneuver generation is 
terminated. 

At any particular decision point, acquisition maneuver 
generation proceeds as follows: 

Choose the maximum available load factor. 
Calculate the resulting alpha. Generate the 
unmaintainable and the unachievable areas for this 
load factor. Test the trial bank angles, starting at 
intercept bank (IB) and working outward (IB, 
IB+5', IB-5', IB+10', ...) discarding those in the 
previously calculated areas. Save the remaining 
trial maneuvers. 

Choose 80% of the maximum available load 
factor. Calculate new areas of unmaintainable and 
unachievable bank angles. As load factor decreases, 
the unmaintainable areas will grow while the 
unachievable areas will diminish. Save only the 
valid trial maneuvers. 

Choose 60% of the maximum available load 
factor and proceed as before. Continue this cycle 
until all trial bins have been filled or all load 
factors have been exhausted. 

Therefore, the highest load factor trial maneuvers for 
which outcomes can be accurately predicted will be 
generated, while physically impossible trial maneuvers are 
prevented from being chosen over realistic trial maneuvers 
which would yield lower angles of attack. These restrictions 
help Paladin make more informed decisions. 


Ground Avoidance 

Each of the maneuver generation schemes uses the 
same ground avoidance logic. Regardless of an engagement 
history, any aircraft that impacts the ground has lost the 
encounter. Considering that most lengthy engagements end 
up at low altitudes, as the participants trade potential energy 
to recover speed, ground avoidance is a significant concern 
throughout Paladin. 

One of the most important aspects of ground avoidance 
is the calculation of the dive recovery angle. This value 
represents the maximum dive angle from which recovery is 
possible with an immediate pull-up given the current 
altitude, Mach number, and bank angle. Since the bank 
angle adds more complexity to the problem than one table 
look-up can reasonably represent, the calculation is broken 
into two parts (roll level then pull up). First, the time 
necessary to roll level is calculated, assuming roll rates 
corresponding to an alpha of the lesser of 10“ or the current 
value. Given this time, an approximate altitude at the pull- 
up point can be predicted from the current altitude and the 
rate of change in the altitude. Then, a table look-up is 
performed based on the current Mach number and the 
predicted altitude, yielding the dive recovery angle. (This 
table was created from data gathered from the aerodynamics 
of the 6 d.o.f. simulation.) Using this dive recovery angle, 
ground avoidance trial maneuvers are generated. 

In order to maintain maneuverability at low altitudes, 
trial maneuver generation must, in addition to providing for 
a pull up when necessary, also give the option of normal 
maneuvers as dictated by the engagement situation. The 
trial maneuver generation algorithm, therefore, is part of 
each of the five maneuver generation schemes discussed 
previously. In addition to normal trial maneuvers, when the 

altitude has fallen below 5000 feet, or the current relative 

0w (the rate of change in the wind axis pitch minus the rate 

of change in dive recovery angle) would put the dive angle 
above the dive recovery angle within 4 decision cycles, or 
the dive angle is currently above the dive recovery angle, 
three extra maneuvers are generated for a ground avoidance 
pull up. These maneuvers are in maneuver planes with 
rotation angles of 0° and ±10" and use a throttle setting of 0 
(idle) if the Mach number is above 0.3 (speed for minimum 
turn radius) or the throttle setting provided by the throttle 
controller otherwise. If any of these maneuver planes 
require the aircraft to roll more than 15", commanded alpha 
is limited to a maximum of 10" for that trial maneuver, thus 
avoiding the slow roll rates encountered at higher alpha. 
Otherwise, the alpha corresponding to maximum available 
lift becomes the trial alpha. 


8 



Together, the generation algorithms and the ground 
avoidance strategies produce a set of trial maneuvers dictated 
by the physical and tactical situation. Paladin must then 
choose one of these trial maneuvers to execute for the 
upcoming period between decisions. 

Prediction of Paladin’s Near Future 

In order to evaluate a trial maneuver, the resulting 
aircraft state must be determined. Unfortunately, integrating 
the full 6 d.o.f. model forward for each maneuver is too time 
consuming for real-time application. It is, therefore, 
necessary to formulate a simplified prediction for this 
resulting state. To do this. Paladin makes certain 
assumptions about the attempted maneuver and its resulting 
flight path. For the look-ahead interval (4 times the 
decision cycle) the following are assumed: 

1) the resulting flight path falls entirely in the 
commanded maneuver plane, 

2) the flight path is a section of a circle (constant 
radius turn), and 

3) the total velocity is constant through this 
segment of the turn. 

With these simplifications, a final position, velocity 
direction, and attitude can be quickly calculated. Like 
extrapolation, prediction will introduce some error into the 
evaluation process. The magnitude of these errors and their 
effects on Paladin's capabilities are discussed later in this 
paper. 

Maneuver Scoring Module 

The Paladin Maneuver Scoring Module knowledge 
source uses a set of fuzzy logic questions 1 with responses 
ranging from [-1.0 = Negative, ..., 0.0 = Neutral, .... 1.0 = 
Positive] and the mode-specific scoring weight vector 
selected by the Situation Assessment Module to score each 
of the trial maneuvers. Each scoring question is intended to 
encourage or discourage some quantifiable aspect of the 
tactical / geometric situation being evaluated. 

Using the data extrapolated for the opponent's future 
state and die data predicted for Paladin's future state resulting 
from one of the trial maneuvers, the relative geometry 
between the future positions of the two aircraft is calculated. 
The score for the maneuver is determined by computing the 
responses to the seventeen fuzzy logic questions given this 
relative geometry, applying the selected scoring weight 
vector, and then summing the results to generate a single 
numeric score. After all of the trial maneuvers have been 
evaluated, the highest scoring maneuver is selected and the 
associated maneuver is commanded. 


Data Error Evaluation 

As discussed earlier, extrapolation and prediction 
introduce error into the maneuver selection process. Some 
amount of error is inevitable since Paladin does not know 
what the opponent will do (or is doing) and since the 
computation time is not available to fully integrate 
Paladin's future options using a high fidelity model. Table 
2 gives the magnitude of these errors as they affect the 
inputs to trial maneuver generation and selection. 

The data shown in table 2 represent a set of 32 batch 
simulations between Paladin and an equivalent opponent, 
containing 10773 decision points. Values give are the 
Mean and Root Mean Squared of the error (actual value 
minus estimated or predicted value) in each of the parameters 
listed. From these numbers, it is evident that Paladin has 
reliable information about both aircraft's future positions, 
velocities, and deviation angles (based only on position and 
velocity). However, those values that depend on orientation 
(line-of-sight, angle-off, azimuth, and elevation) are 
significantly less reliable. (As would be expected, Paladin 
can predict its own orientation better than that of the 
opponent.) Given only these results, it is difficult to assess 
the impact of the errors, as the relative importance of these 
data as well as the sensitivity of the selection process will 
heavily affect the results. 

To see the full effect of the estimation and prediction 
errors, it is necessary to find the ways that these errors 
change the decision process. For the same set of 32 runs 
used for table 2, the following effects were accumulated: 


Table 2. Errors Due to Extrapolation and Prec 

liction 

Error in: 

Mean 

RMS 

Range (ft.) 

0.028 

1.792 

Range Rate (ft./sec.) 

0.298 

15.279 

Paladin's Z coordinate (ft.) 

0.118 

1.354 

Paladin’s Dive Angle (deg.) 

0.016 

1.110 

Paladin’s Dive Recovery Angle (deg.) 

-0.055 

1.341 

Paladin's Line-of-Sight Angle (deg.) 

0.999 

7.259 

Paladin's Deviation Angle (deg.) 

0.167 

1.100 

Paladin's Angle-Off (deg.) 

0.474 

11.239 

Paladin’s Azimuth Angle (deg.) 

-0.223 

23.845 

Paladin’s Elevation Angle (deg.) 

-0.205 

14.597 

Opponent’s Deviation Angle (deg.) 

-0.010 

1.033 

Opponent’s Line-of-Sight Angle (deg.) 

-0.474 

11.239 

Opponent’s Angle-Off (deg.) 

-0.999 

7.259 

Opponent’s Azimuth Angle (deg.) 

-0.473 

26.330 

Opponent’s Elevation Angle (deg.) 

3.832 

18.400 


9 




- Paladin chose the wrong maneuver generation 
algorithm in 0.0362% of the decisions (where 
wrong means different from the choice that 
would have been made with perfect 
information), 

- Paladin chose the wrong trial maneuver due to 
extrapolation error in 6.823% of the decisions, 
which lead to a projected scoring loss of 
7.253* 10' 7 out of an average score of 6.248 on 
those decisions, 

- over all the decisions, the mean scoring error 
due to extrapolation errors was 0.099 with an 
RMS value of 0.766 out of an actual score with 
mean 1.422 and RMS 11.375, 

- over all the decisions, the mean total scoring 
error due to extrapolation and prediction errors 
was 0.593 with an RMS value of 1.960 out of 
an actual score with mean 1.422 and RMS 
11.375. 

It can be seen, therefore, that the extrapolation and 
prediction errors do have an effect on Paladin’s decision 
making. Mistakes are made in choosing the maneuver 
generation algorithm, as well as the “best” trial maneuver. 
Nonetheless, these effects are both infrequent and relatively 
small in resulting loss of capability (as measured by 
maneuver score). 

Paladin Testing Procedures 

Paladin is currently being tested in the TMS using 6 
d.o.f. aircraft dynamics, and in the DMS using 5 d.o.f. 
aircraft dynamics 1,6 . TMS testing is done in a non-real- 
time, batch mode environment against a baseline TDG. A 
group of test conditions consists of 32 sets of initial aircraft 
conditions. The initial altitudes, airspeeds, and the 
separations between the two aircraft are adjusted to provide 
representative coverage of the within-visual-range air 
combat arena. 

A set of engagement scoring metrics 1 is reviewed after 
each group of test runs and the data are used to tune the 
mode specific scoring weights and test the completeness of 
the knowledge bases. Although the metrics are helpful, no 
single metric has been developed that can completely 
measure the performance of an aircraft in the engagement 

After initial adjustment of the scoring weights, the set 
of initial conditions is expanded to 320 initial conditions by 
- modifying the initial separation between the airplanes, the 
initial altitudes, and the initial Mach numbers. This 
stepwise refinement process provides the large set of results 


required to achieve global system improvements across the 
total within-visual-range air combat environment. 

A baseline version of Paladin is currently being tested 
in the DMS using a 5 d.o.f. aircraft model. The aircraft 
model lacks both the extra degree of freedom (lateral motion 
in body axes) as well as an accurate representation of the 
aircraft’s rotational dynamics throughout the complete flight 
envelope. This predecessor to the Paladin system, the 
Computerized Logic for Air Warfare Simulation (CLAWS) 
contains the situation assessment module, the active throttle 
controller, and a similar set of situationally dependent trial 
maneuvers. 

The development of CLAWS made possible the 
evaluation of the tactical decision generation software 
against human pilots in the DMS. This capability has 
allowed experienced pilots to interact with the system in a 
realistic air combat environment, comment on its 
performance, and suggest improvements. The pilots' 
comments and suggestions are then the basis for changing 
the TMS experimental version of Paladin. These changes 
are tested and refined before being included in the baseline 
system. In order to extend this valuable interaction. Paladin 
and the 6 d.o.f. model will shortly be implemented in the 
DMS. 

Con clu sions 

Paladin, a computerized air combat tactical decision 
generator, has been developed to study air combat 
engagements. The system incorporates modem aircraft 
simulation techniques, sensors, and weapons systems. 
Paladin uses knowledge-based systems to address air-to-air 
combat and agile aircraft in a clear and concise manner. The 
Differential Maneuvering Simulator offers a unique 
opportunity to evaluate the performance of the Paladin 
software in a real-time tactical environment against human 
pilots. 

Paladin models aspects of the decision-making 
processes used by human pilots through the generation of 
situationally dependent sets of trial maneuvers and the mode 
sensitive scoring of their predicted outcomes. The use of 
distinct modes of operation allows Paladin to perform 
complex air-to-air combat tasks and generate sound tactical 
decisions in real-time. Without the situationally dependent 
mode selection, Paladin would be forced to either sacrifice 
its real-time execution or assume an unrealistic tactical 
mind-set for the duration of the engagement. 

Although predicting information about the outcome of 
a maneuver introduces errors into the decision making 
process, and these errors do interfere with the selection of 
the desired course of action, they do not greatly affect the 


10 



overall capabilities of Paladin. Any errors that would 4. 
greatly change the decision outcome are naturally 
discounted, as the tuning process will place less importance 
on unreliable data. However, as a result, Paladin also will 
be more sensitive to new errors in previously reliable data. 
Retuning is, hence, important for Paladin whenever a data 5. 
item is changed or added. 

References 

1. Goodrich, Kenneth H.; John W. McManus: "An 
Integrated Environment For Tactical Guidance 6. 
Research and Evaluation." AIAA Paper #90-1287 
May 1990. 

2. Goodrich, Kenneth H.; Dr. John W. McManus; Alan 
R. Chappell: "A High-Fidelity Batch Simulation 
Environment for Integrated Batch and Piloted Air 7. 
Combat Simulation Analysis" AIAA Paper #92- 
4145, August 1992. 

3. Meyer, Bertrand. Object-oriented Software 
Construction. Ed. C.A.R. Hoare. Prentice Hall 
International Ltd, 1988. 


McManus, John W.: "A Parallel Distributed System 
for Aircraft Tactical Decision Generation” In 
Proceedings of the 9th Digital Avionics Systems 
Conference, 1990, pp. 505 - 512. 

Burgin, G. H., et al.: An Adaptive Maneuvering 
Logic Computer Program for the Simulation of One- 
on-One Air-to-Air Combat. Vol I and Vol II. 
NASA CR-2582, CR-2583, 1975. 

McManus, John W.; Kenneth H. Goodrich: 
"Application of Artificial Intelligence (AI) 
Programming Techniques to Tactical Guidance for 
Fighter Aircraft." AIAA Paper #89-3525, August 
1989. 

McManus, John W.; Alan R. Chappell; P. Douglas 
Arbuckle: "Situation Assessment in the Paladin 
Tactical Decision Generation System.” AGARD 
Conference Proceedings 504; Air Vehicle Mission 
Control and Management, March 1992. 


11 



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January 1993 Technical Memorandum 


4. TITLE AND SUBTITLE 5. FUNDING NUMBERS 

Trail Maneuver Generation and Selection in the Paladin Tactical Decision 505-64-30-01 

Generation System 


6. AUTHOR(S) 

Alan R. Chappell, John W. McManus, and Kenneth H. Goodrich 


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

NASA Langley Research Center 
Hamtpon, VA 23681-0001 


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National Aeronautics and Space Administration 
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AGENCY REPORT NUMBER 

NASA TM-1 07722 


11. SUPPLEMENTARY NOTES 


Chappell: Lockheed Engineering and Sciences Company, Hampton, Virginia 
McManus and Goodrich: Langley Research Center, Hampton, Virginia 


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Subject Category 08 


13. ABSTRACT (Maximum 200 words) 

To date, increased levels of maneuverability and controllability in aircraft have been postulated as tactically advantageous, 
but little research has studied maneuvers or tactics that make use of these capabilities. In order to help fill this void, a 
real-time tactical decision generation system for air combat engagements, Paladin, has been developed. Paladin models an 
air combat engagement as a series of discrete decisions. A detailed description of Paladin's decision making process is 
presented. This includes the sources of data used, methods of generating reasonable maneuvers for the Paladin aircraft, 
and selection criteria for choosing the "best" maneuver. Simulation testing results are presented that show Paladin to be 
relatively insensitive to errors introduced irtto the decision process by estimation of future positional and geometric data. 


14. SUBJECT TERMS 

Air Combat Simulation, Tactical Maneuver Generation, Estimation Error, Distributed 
Computing, Situation Assessment, and Aircraft Maneuver Limitations 


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