The results are presented of a study which uses fuzzy sets to model a Space Shuttle pilot's reasoning and actions while performing rendezvous proximity operation maneuvers. In this model fuzzy sets are used to simulate smooth and continuous actions as would be expected from an experienced pilot and to simulate common sense reasoning in the decision process. The present model assumes visual information available to the Shuttle pilot from the Shuttle Crew Optical Alignment Sighting (COAS) device and the overhead window and rendezvous radar sensor information available to him from an onboard display. This model will be used in a flight analysis simulator to perform studies requiring a large number of runs, each of which currently needs an engineer in the loop to supply the piloting decisions. This work has much broader implications in control of robots such as the Flight Telerobotic Servicer, in automated pilot control and attitude control, and in advisory and evaluation functions that could be used for flight data monitoring or for testing of various rule sets in flight preparation

Goodwin, Mary Ann

Lea, Robert N.

Mitchell, Eric V.

English

NASA Technical Reports Server

AUTONOMOUS CONTROL PROCEDURES FOR SHUTTLE 
RENDEZVOUS PROXIMITY OPERATIONS 
Robert N. Lea and Eric V. Mitchell 
NASNJohnson Space Center, Houston, Texas 
Mary Ann Goodwin 
Rockwell Shuttle Operations Company 
ABSTRACT 
This paper presents the results of a study which 
uses fuzzy sets to  model a Space Shuttle pilot's 
reasoning and actions wh i le  per forming 
rendezvous proximity operation maneuvers. 
Use of conventional pilot models is limited since 
they often results in unrealistic overfirings and, 
therefore, excess fuel usage and unacceptable 
p lay load  c o n t a m i n a t i o n  a n d  p lume 
disturbances. In this model fuzzy sets are used 
t o  simulate smooth and continuous actions as 
would be expected from an experienced pilot 
and t o  simulate common sense reasoning in the 
decision process. The present model assumes 
visual information available to the Shuttle pilot 
from the  Shuttle Crew Optical Alignment 
Sighting (COAS) device and the overhead 
w i n d o w  and  rendezvous radar  sensor 
information available to  him from an onboard 
display. 
This model will be used in a f l ight analysis 
simulator t o  perform studies requiring a large 
number of runs, each of which currently needs 
an engineer in the loop to  supply the piloting 
decisions. A great deal of the engineer's time is 
required for this repetitious and somewhat 
boring function. This work has much broader 
implications in  control of robots such as the 
Flight Telerobotic Servicer, in automated pilot 
control and attitude control, and in advisory and 
evaluation functions that could be used for 
f l ight data monitoring or for testing of various 
rule sets in flight preparation. 
INTRODUCTION 
The manual phase of Space Shuttle rendezvous 
begins several nautical miles from the target 
when the crew starts maneuvering the Shuttle 
using visual and sensory information. This 
portion of rendezvous is referred t o  as terminal 
phase. 
Once terminal phase begins the crew can fly a 
number of scenarios, or paths, t o  approach the 
target. One sequence is illustrated in figure 1. 
The coordinate system is centered at the target, 
and has a v-bar axis tangential t o  the target's 
orbit and positive in i t s  direction of motion. The 
r-bar axis is perpendicular t o  the v-bar axis with 
the positive direction towards the center of the 
earth. The Shuttle flies to  the v-bar axis several 
hundred feet in front of the target. There the 
upward velocity is  nulled t o  zero, and the  
vehicle begins "closing" toward the target by 
decreasing i ts  velocity relative t o  the target's 
velocity so tha t  t h e  t w o  w i l l  eventual ly 
rendezvous. 
ET 
TARGET AT CENTER OF 
ROTATING LVLH REFERENCE 
FRAME 
1. TERMINAL PHASE 
2. VBAR APPROACH 
3. SEPARATION R 
figure 1 
One simulation available for these so called 
"p rox imi ty  ope ra t i ons "  i s  t h e  Shu t t l e  
Engineering Simulator (SES), a high fidelity 
simulation complete w i t h  cockpit.  It i s  
expensive to  operate, heavily scheduled and 
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https://ntrs.nasa.gov/search.jsp?R=19880007860 2020-03-20T07:31:26+00:00Z
must frequently be reserved for crew training. 
For certain engineering studies that do not 
recpire SES fidelity, a "desk top" simulator [1,2] 
implemented on an HP 9000 is adequate. A 
"pilot" controls the "vehicle" by input through 
a keyboard or, sometimes, a joystick. Decisions 
to  "fire jets" are made based on CRT graphical 
representations of the view through the Shuttle 
overhead window, the scenes from closed circuit 
television cameras mounted in the payload bay 
and the view through the Crew Optical 
Alinement Sight (COAS). Digital data on the 
CRT represents information provided the 
Shuttle crew through onboard CRT displays. 
Included in this data are rendezvous radar 
measurements of range and range rate, which 
are updated a t  the simulation integration rate. 
There are several motives for wanting t o  
program an automatic pilot that can "fly" this 
simulation. An automatic pilot that realistically 
represented human response could make it 
possible to  obtain many executions in a batch 
mode that presently require an engineer in the 
loop. If a number of executions are needed to 
obtain statistics on various parameters, such as 
f ue l  usage, contaminat ion,  or  plume 
impingement, a great deal of engineering time 
can be required. Run time could be reduced 
since creation of  the graphic output at  a 
realtime response rate could be eliminated. 
Further, once an engineer playing pilot has 
performed ten or so simulations of a proximity 
operations scenario, the effort of learning for 
that scenario is unrealistically high, which causes 
the results to  be overly optimistic. 
Existing automatic pilots for a batch-mode 
simulation were found to be almost as labor 
intensive as having an engineer in the loop. A 
given scenario might need to  be executed 
repeatedly and "tuned" to determine when and 
what thrusters should fire before the desired 
results can be obtained. This effort was 
necessary t o  avoid the overfirings tha t  
frequently resulted in excess fuel usage and 
unaccecptable payload contamination and 
plume disturbances due to the "crispness" of 
the commands in the logic coded to  do the 
piloting. 
The need then is to create decision making logic 
that results in the same common sense decisions 
a pilot would make. Since the pilot uses his 
experience combined with the imprecise visual 
and digital information available to  him, it 
appeared that fuzzy logic would provided a 
good basis for simulating his decision making. 
Furthermore, it was realized that this approach 
could be adapted to  a number of  areas of 
interest, such as development of translational 
and rotational digital autopilots, telerobotics, 
remote vehicle control, as well as ground or 
onboard advisory and evaluation functions. 
A low level study effort has been underway for 
several months. The results and status to  date 
are presented below. 
DESIGN AND IMPLEMENTATION 
Design and implementation of the p i l o t  
proceeded as follows. A set of rules of the kind 
observed and followed by Shuttle pilots flying a 
proximity operations scenario o f  the type 
illustrated in figure 1 was developed by 
observing and communicating with pilots of 
simulators used in  Shuttle t ra in ing and 
evaluation and testing at the Johnson Space 
Center. These rules were stated in natural 
language as they were related to  us by the 
various pilots. The rules deal with the Shuttle 
keeping both the desired vertical distance and 
the desired closing velocity with respect t o  the 
target. The visuals that were used in this study 
were restricted to the COAS and the overhead 
window, which are illustrated in figure 2, and 
the digital data display of range and range rate 
from the rendezvous radar. 
figure 2 
282 
Typical rules maintaining the desired vertical 
relationship are of the following form: 
If the target is located near the center of 
the field of view of the COAS , then no 
action is  needed to  raise or lower the 
shuttle. 
If the target is significantly above or below 
the center of the field of view of the COAS, 
then jets must be fired to lower or raise the 
shuttle. 
The following rule is used to  maintain the 
desired horizonal closing relationship: 
If the Shuttle is at a braking gate, e.g., 
1000 ft, 500 ft, 400 ft, ..., from the target, 
then the closing velocity should be 1 .O fps, 
.5 fps, .4fps, ... 
To implement these rules using fuzzy sets it was 
necessary to  examine more closely what was 
meant by phrases such as "near" the center and 
"significantly" above or below. These terms are 
definitely descriptors o f  sets wi th  fuzzy 
boundaries. 
It was decided to use the n and S functions as 
given in [3, 41 since they can easily be adjusted 
for different degrees of "fuzziness" by varying 
the parameters that define their width and 
shape. The equations of the n and S functions 
are given below. 
S(x,a,b,c) = 0 f o r x I  a 
= 2((x - a)/(c - a))**2 for a I x I b 
= 1 - Z((x - c)/(c - a))**2 forb I x 5 c 
= 1  forx 2 c 
n (x,b,c) = S(x, c - b, c - b/2, c) for x 5 c 
= 1 - S(x, c, c + b/2, c + b) for x 2 c 
(x; a, b. c 1 P 
0 a b C X 
a - b  a + b  x 
b 
figure 3 
As can be seen, one can effect a rapid or slow 
transisition from complete membership t o  
complete non-membership by altering the 
parameters a, b, and c or b and c for the S or n 
function respectively. For example, the graphs 
of the 5 and n functions used f o r  t he  
maintainance of vertical position are given in 
figure 4. These functions allow flexibility in 
simulating different style pilots, for example, 
those who attempt to  keep the target very close 
to the center of the field of view at all times, 
those who are more relaxed and are concerned 
mostly with keeping the target in view and 
using orbital mechanic effects t o  the i r  
advantage, and any type of pilot between these 
extremes. 
Graphs of the general S and n functions are 
given in figure 3. 
283 
TARGET 
VERTKAL ANGLE 
figure 4 
The functions are used as follows. Fuzzy sets 
were defined for "somewhat greater than", 
"somewhat less than", and "approximately 
equal to" the desired closing rate. They were 
also defined for "high", "low", and "near the 
center" in the field of view. Approximately 
every twenty seconds, a time interval considered 
sufficient for a pilot to deal with the visual and 
displayed noisy range and range rate 
information, the fuzzy sets were evaluated and 
the maximum value recorded for each set of 
three functions corresponding respectively to  
closing rate and location in the field of view. 
Indices identifying the fuzzy function with the 
larger value for each set were recorded. If both 
indices corresponds to  the no change fuzzy 
functions then no action is taken, if exactly one 
indicates n o  change then  the  act ion 
corresponding to  the other maximum value is 
taken, and if both indicate action should be 
taken then the action corresponding to  the 
larger of the two is taken. 
The action to  be taken, which is  a certain 
number of jet firings, is determined as follows. 
The velocity change required to  effect a vertical 
position change or to  increase or decrease the 
closing rate is divided by the proper setting of 
the digital autopilot (DAP). The DAP has two 
settings that are preloaded with values that 
control the magnitude of the jet firings. Typical 
values are 0.02 and 0.05 which translates into 
0.02 or 0.05 feet per second change in velocity 
per pulse depending on which value has been 
selected. The nominal DAP setting is the larger 
of the two and is the proper setting if it is  
smaller than the required velocity change. If this 
setting exceeds the required velocity change 
then the proper setting is the smaller value. The 
selected number, which could be considered the 
appropriate number of pulses under ideal 
conditions, is then weighted by multiplying by 
the fuzzy set evaluation that has been saved. 
This is the number of pulses that is commanded 
to the jets in the required direction. 
To illustrate the number of pulses computation 
consider figure 5. 
figure 5 
In this case the target is high in the field of view. 
By using the shuttle-target range the angle A 
can be used to compute s, the distance below 
the target. Using the equation Ah = .56 Av, 
which relates Ah in nautical miles to  Av in feet 
per second one can estimate the required Av to  
move the shuttle up to the v-bar. This estimate 
of Av is adjusted according to  whether the 
shuttle is currently moving up or down relative 
the target. This Av can then be converted into 
an "ideal" number of pulses by dividing by the 
DAP setting, d. If f(A) is the evaluation of the 
fuzzy function- corresponding to target high in 
the field of view then the number of pulses to  
be applied is given by 
N = (Av/d)*f(A) 
Closing rate control is easier in theory since one 
only has to difference actual closing rate and 
desired closing rate and divide by the DAP 
setting. In practice however it is considerably 
harder since range rate obtained from the radar 
is corrupted by noise and bias which at close 
ranges can be on the order of magnitude of the 
closing rates that need to  be maintained. This 
problem, which is essentially one of modeling a 
human method of visually monitoring a stream 
of data and extracting the center of the data, is 
currently being considered. Some initial efforts 
look promising but have not  been tested 
sufficiently at this time. 
284 
RESULTS 
9 
The automatic pilot can presently perform a 
terminal phase approach to  the v-bar and a 
closing approach along the v-bar and maintain 
the desired line of sight and closing rates in a 
manner that compares reasonably well to the 
response of a "nominal" pilot. A "nominal" 
pilot is considered one that performs maneuvers 
in a manner recognized as appropriate in flight 
planning material. "Nominal" means the pilot 
does not allow the Shuttle's position and 
velocity to  deviate greatly from the planned 
scena ri 0. 
The fo l l ow ing  table shows propel lant  
consumption data tabulated from flying five 
dispersed proximity operation profiles of the 
type illustrated in figure 1. Both manually 
controlled and automated pilot controlled are 
given and it can be seen that the automated 
pilot controller compares favorably to  the 
manually flown profile. It is a t  least within ten 
percent for each case. Dispersed means that the 
beginning states are randomly selected and that 
the noise and bias in the rendezvous radar are 
randomly varied within radar specification 
limitations. 
11 
ORBITER RCS PROPELLANT CONSUMPTION 
ORBITER V-BAR APPROACH 
FROM SO0 TO 60 FT 
FLIGHT PROFILE SCENARIOS 
MIL - MAN - IN - A -LOOP 
**PROPELLANT 
CONSUMPTION 
TOTAL (LBS) 
101.0 (MIL) APPROACH 
DESPERSED CASE 1 
I 107.0 I (MIL) APPROACH I DESPERSED CASE 2 
126.0 (MIL) APPROACH 
DESPERSED CASE 3 
119.0 
DESPERSED V S E  4 
I I 116.0 (MIL) APPR~ACH DESPERSED CASE 5 I 
110.0 AUTO -PILOT APPROACH 
DESPERSED CASE 1 I 
I 115.0 AUTO ~ PILOT APPROACH DESPERSED CASE 2 I 
120.0 AUTO ~ PILOT APPROACH 
DESPERSED CASE 3 
127.0 
DESPERSED CASE 4 
122.0 AUTO - PILOT APPROACH 
DESPERSED CASE 5 
** PROPELLANT NUMBERS ARE NOT A 
STATISTICAL MEAN BUT ARE REPRESENTATIVE 
OF THE NUMBER OF FLIGHT PROFILES FLOWN 
Figure 6 illustrates a typical man-in-the-loop 
trajectory as contrasted with a profile generated 
by the automated pi lot.  Note that  the 
trajectories are similar for the two cases.. 
TYPICAL V-BAR APPROACH TRAJECTORY EXAMPLE 
MAN -IN -THE - LOOP(M1L) VS. AUTOMATED PILOT 
L 100 X ( F l T )  
AUTOMATED PILOT r -loo 
X I F T )  
figure 6 
L 100 
STATUS AND CONCLUSIONS 
The preliminary results of an automatic pilot 
for a low-fidelity Shuttle proximity operations 
simulator that maneuvers based on fuzzy 
decision functions indicate the goals of  the 
study is achieveable. That is, it appears to  be 
possible t o  simulate the common sense 
reasoning of a pilot using f u z z y  decision 
functions to  express rules obtained from 
experienced pilots. 
A great deal of work must be completed to  
recognize the full potential of the concept, 
however. First, we realize improvements can be 
nade to the existing rules. These improvements 
Jvill give more versatility to the reasoning and 
will incorporate additional fuzzy decision 
functions. An example is to give more emphasis 
to the vertical rate of the Shuttle to  decide if the 
Shuttle needs to  be raised or lowered. The 
present version does not deal with out-of-plane 
errors. In addition there are many more 
scenarios to address. 
285 
For expediency, the automatic p i lo t  was 
implemented in Fortran, but it is apparent that a 
rule based expert system shell would provide a 
better implementation language and a 
conversion will be made assoon as possible. 
While the parameters chosen for the curves used 
in the fuzzy functions result in appropriate 
decisions for a wide range of input for a given 
scenario, it may be desireable to  allow the user 
to  determine the piloting characteristics he 
would like simulated, and have the software 
select the parameters based on this description. 
For example, does the pilot respond quickly to 
deviations from the desired path, or does he 
allow errors larger than considered "average" 
to  accumulate before he reacts. Results indicate 
a method could be devised to accomplish this. 
As the approach i s  extended t o  other 
applications, or possibly to  speed up use of the 
present application, it is realized that a fuzzy 
function chip could be used to  offload a great 
deal o f  the computation. This would be 
especially appropriate to  study the application 
of the concept to  realtime rotational and 
translational digital autopilots. 
ACKNOWLEDGEMENTS 
The authors wish to acknowledge Sam Wilson, 
TRW Systems, and John Whynott, McDonnell 
Douglas Corporation, f o r  the i r  various 
contributions which range from dealing with 
the man-in-the-loop software simulation to  
augmenting our understanding of  Shuttle 
proximity operations. 
REFERENCES 
1. Whynott, J.J., Hewlett Packard 9000 Desk Top 
Flight Simulator version 1.0 Preliminary 
Documentation, October, 1985. 
2, Wilson, Sam, Delivery of Software for HP- 
9825A Desk Top Flight Simulator (DTFS) Version 
04F, TRW Letter No. 84:W482.1-5, February, 
1984. 
3. Zadeh, L.A., Fu, K.S., Tanaka, K, Shimura, M. 
(eds.), Fuzzy Sets and Their Applications t o  
Cognitive and Decision Processes, New York- 
London, 1975. 
4. Zimmerman, H.J., Fuzzy Set Theory and i t s  
Applications, Kluwer-Nijhoff, Boston-Dordrecht- 
Lancaster Publishing 1985. 
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Autonomous control procedures for shuttle rendezvous proximity operations

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