The Story of

 Self-Repairing

Flight Control Systems

By James E. Tomayko

Edited by Christian Gelzer Dryden Historical Study No. 1
i

The Story of

Self-Repairing

Flight Control Systems

By James E. Tomayko

Edited by Christian Gelzer

Dryden Historical Study No. 1
ii
iii

On the Cover:

On the front cover is a NASA photo (EC 88203-6), which shows an Air Force F-15 flying despite the

absence of one of its wings. The image was modified to illustrate why self-repairing flight control systems

might play a vital role in aircraft control.

Between the front and rear covers, the lattice pattern represents an electronic model of a neural net, essential to the development of self-repairing aircraft.

On the back cover is the F-15 Advanced Control Technology for Integrated Vehicles, or ACTIVE, (EC96

43780-1), which was the primary testbed for self-repairing flight control systems.

The Story of

 Self-Repairing

Flight Control Systems

By James E. Tomayko

Edited by Christian Gelzer Dryden Historical Study No. 1

Book and cover design by Jay Levine, NASA Dryden Flight Research Center
iv

Table of Contents

Acknowledgments .......................................................................................................................................... VI

Sections

 Section 1: A Brief History of Flight Control and Previous Self-Repairing Systems..................................... 1

 Section 2: The Self-Repairing Flight Control System................................................................................... 12

 Section 3: The Intelligent Flight Control System .......................................................................................... 29

Epilogue: The Future of Intelligent Flight Control................................................................................ 45

Glossary ............................................................................................................................................................ 52

Abbreviations.................................................................................................................................................. 54

Bibliography .................................................................................................................................................... 55

Appendices

 Appendix A: SRFCS Flight Log.................................................................................................................... 60

 Appendix B: Flight Test Summary, SRFCS F-15A No. 8 RTO: NASA Ames-Dryden ................................ 61

 Appendix C: SRFCR Progress Report, April 5, 1990 ................................................................................... 72
v

 Appendix D: SRFCR Progress Report, March 29, 1990 ............................................................................... 75

 Appendix E: SRFCR Progress Report, March 22, 1990 ............................................................................... 77

 Appendix F: SRFCR Progress Report, March 15, 1990................................................................................ 79

 Appendix G: SRFCR Progress Report, March 8, 1990 ................................................................................. 80

 Appendix H: SRFCR Progress Report, March 1, 1990 ................................................................................. 83

 Appendix I: SRFCR Progress Report, February 15, 1990............................................................................. 85

 Appendix J: SRFCR Progress Report, February 8, 1990 .............................................................................. 87

 Appendix K: SRFCR Progress Report, February 1, 1990 ............................................................................. 89

 Appendix L: IFCS FRR, Structures (February 1999 Review) ...................................................................... 91

 Appendix M: IFCS FRR (February 1999 Review)........................................................................................ 93

 Appendix N: IFCS FRR, System Operation (February 1999 Review) ......................................................... 102

 Appendix O: Report WL-TR-91-3025, Vol I Self-Repairing Flight Control, August 1991 .......................... 106

 Appendix P: Pilot Reports (Boeing Company 99P0040) Intelligent Flight Control Final Report................ 109

 Appendix Q: Flight Test Matrix (Boeing Company 99P0040) Intelligent Flight Control Final Report ....... 138
vi

My deepest thanks to J. D. “Dill” Hunley, who was the chief historian at Dryden while I was writing this

monograph. He is one of the best historians, inside or outside the government, with whom I have had the

pleasure to work. Michael Gorn, who succeeded Dill when this manuscript was between preparation and

publishing, has my thanks for his revisions of the manuscript and other efforts. Also, an important thank you to

Jim Stewart, who sponsored this history of the continuing Intelligent Flight Control System. He has a keen

awareness of the value of history.

I would also like to express my appreciation to those who shared precious time for an interview or patiently

answered extensive questions. Their information and insights led to a more accurate publication. From NASA

Ames Research Center: Chuck Jorgenson, John Kaneshige and Joe Totah. From NASA Dryden Flight Research

Center: Bill Burcham, John Carter, Jerry Henry, Wilt Lock, Gerard Schkolnik, Jim Smolka, and, of course, Jim

Stewart. From NASA Langley Research Center: Aaron Ostroff. From the Boeing Phantom Works: Jim Urnes,

Sr. Lastly, from the U. S. Air Force’s Wright Laboratory: Bob Quaglieri.

I would have been lost without my student assistant, Sara Lechleitner, whose word processing skills are

almost frightening. She also has a work ethic that enables her to play intercollegiate volleyball, pursue a

mechanical engineering degree with an outstanding academic record, and (incidentally) help me. She did much

of the glossary, acronym list, footnotes, as well as some insightful editing.

Thanks to my wife, Laura Lallement Tomayko, who scanned the line drawings, and to Dryden’s Carla

Thomas, for scanning the photographs. Also thanks to Dryden layout wizard Jay Levine.

Finally, to Allison Xingbi Tomayko, whose adoption focused my efforts on telling this story, I gratefully

dedicate it.

Acknowledgments

– James E. Tomayko

 April 2003
1

Section 1: A Brief History of Flight Control

 and Previous Self-Repairing Systems

Introduction

The ochre desert floor was a blur beneath the streaking Israeli Air Force F-15. From 12,000 feet, the pilot

and his back-seat instructor could barely make out the haze of the Mediterranean to the west, and the teeming

cities to the north. The two-ship formation was simulating the defense of his air base against two pairs of agile

A-4s. Even though his plane represented technology 20 years newer than the A-4s, he had great respect for the

small, maneuverable jet (which later earned notice by the public as the Navy’s aggressor plane of choice at the

Top Gun School, in the movie of the same name). The pilot came up from behind the trailing pair of A-4s,

looking for the classic Sidewinder missile shot.

The F-15s ran with their radars off so as not to radiate electronic emissions and give away their position to

the “enemy”, only now-and-again turning them on for a sweep. The subsequent snapshot of their adversaries’

position was all the F-15s had to go on, since the relatively tiny, camouflaged A-4s were difficult to acquire

visually from more than a few hundred meters away. The A-4s, meanwhile, ran in on the deck, hoping to be

invisible against the ground clutter from the lookdown radar of the F-15s.

Suddenly one of the A-4s started a “pop-up” maneuver in which it quickly gained altitude to unmask the

radar hunting it. As he climbed, the pilot of the A-4 rolled his plane inverted, allowing him to see clearly

beneath him; this also meant his wings blocked any upward view. Assuming his “adversary” was now beneath

him, the A-4 driver did not see the F-15 that was now, in fact, right above him, and he collided with the F-15’s right

wing, destroying his own airplane. The A-4 pilot ejected as his plane exploded into a ball of fire.

After the F-15 pilot recovered from the sudden and literal shock, he saw from his instruments that his jet

was venting fuel at an alarming rate, emptying one entire wing tank in no time at all. The F-15 then entered a

slow roll to the right at about 350 knots, and began to lose altitude, heading into a spiraling dive. The Israeli

pilot had the fleeting notion of ejecting, (his back-seat instructor was telling him to do just that) but he added

power instead and stomped hard left rudder in order to pull out. He managed to arrest the spin and then to

regain altitude, meanwhile calling for vectors to the nearby F-16 base at Ramon.

Declaring an emergency, he began reducing the big plane’s airspeed and losing altitude for landing. When

the F-15 approached 260 knots, roughly twice its usual approach speed, it went into another spin. Again the
2

pilot successfully fought the urge to eject, and again he managed to stop the spin using control surface inputs

and resorting to the afterburners.

Approaching the runway, the F-15 touched down at 250 knots—the pilot dared go no slower for fear of

losing control again. The runway at Ramon was equipped with arresting gear like that found on aircraft

carriers, put there for use in emergencies. The F-15 caught an arresting wire with its tailhook, which promptly

ripped away from the plane due to its excessive speed. But now slowed to 150 knot, the pilot stood on the

brakes and brought the plane to a stop a mere 10 meters from the emergency recovery net erected at the other

end of the runway. Less than five minutes elapsed between collision and touchdown.

With a great sigh of relief, the pilot removed the helmet from his heavily perspiring head, stood up and

turned toward his back-seater for a congratulatory handshake. Only then did he see the real damage to his

aircraft. The entire right wing had been sheared off. The incredulous pilot spent a long time looking at where

the wing had been. An F-16 pilot walked up to the crippled plane from the left side, came around the nose,

saw the damage, and purportedly asked the pilot, “Where do I sign up for F-15s?”1

At McDonnell Douglas Corporation, the plane’s maker, engineers marveled at how this F-15 managed to

stay right side up, to say nothing of how the pilot managed to control the approach. Though curious about the

whole affair, the engineers had no data for such extensive damage, and so they placed an F-15 wind tunnel

model in MDC’s high-speed wind tunnel, sawing off successive sections of the right wing between tests. In

the end, calculations done by controls engineers discovered that the margin to maintain controllable flight for

this extent of wing damage was only ±20 knots and ±20 degree angle of attack variation from trim. The

engineers were amazed that the IAF pilot found and maintained this very narrow margin of control. And the

revelation that there was a stable flight condition for such serious damage triggered a much higher interest in

reconfigurable controls technology.

These events occurred in May 1983, and the F-15 was subsequently repaired and returned to service. The

story spread far and wide, leading some to ask if this was simply another case of ‘even a brick can fly if you

hang a big enough engine on it?’ The answer was yes, at least in part.

MDC engineers concluded that the damaged F-15 stayed aloft because at high speeds its fuselage

generated just enough lift to compensate for the missing wing. Bleeding off speed brought on a spin, while

maintaining high speed kept the plane in the air. The question begged itself: could a plane’s flight controls be

reconfigured automatically by special control software so that in case of such damage the pilot could fly a

1

 News accounts say the pilot was on a training mission with an instructor in the back seat. Some of these reports further add that the

pilot was subsequently demoted one rank for disobeying orders to eject, and promoted two ranks for safely recovering the aircraft.
3

An Israeli Air Force F-15 involved in a midair collision during a training mission in 1983. In spite of losing

virtually the entire starboard wing, the pilot successfully landed the jet.

NASA Photo EC89 232-1
4

crippled airplane slowly enough to land safely? The U.S. Air Force decided to find out. In partnership with

NASA, and using NASA’s HIDEC F-15 test-bed, the two agencies tested the Self-Repairing Flight Control

System (SRFCS) between 1989-90. To further advance this damage adaptive technology, in 1995 NASA and

McDonnell Douglas cooperated in developing a new system to install in a special research F-15 equipped

with a fly-by-wire control system. The newer system is called the Intelligent Flight Control System (IFCS)

largely because it uses artificial neural networks to “learn” how to fly a partly failed or battle-damaged

airplane. Aside from the obvious safety advantages, a neural net may result in a cheaper, faster-to-build flight

control system because it can “learn” to fly a new aircraft more quickly and cheaply than a flight control

system can be designed.

Flight Control Surfaces

An understanding of conventional flight controls clarifies how the SRFCS and the IFCS perform their

jobs. The concept of controling an aircraft in flight originated with Sir George Cayley, who, in 1799, became

the first person to design a prototype airplane. Cayley’s design included a recognizable fuselage, wings, and

a cruciform tail. Steering came through a conventional boat’s rudder.2 What appeared in Cayley’s drawings

and subsequent models (he built and purportedly tested at least one model large enough to carry a human)

were aircraft with the essential elements found in modern aircraft, including lifting surfaces and rudimentary

control surfaces in the tail. (There were no control surfaces on the wings, however.) Although his work

influenced many early aeronauts regarding such things as lift and control, as late as the nineteenth century

some designers continued to ignore control surfaces on airplanes. Otto Lilienthal, the first human to die in a

heavier-than-air craft, is one example. In the last decade of the nineteenth century Lilienthal flew elegant

hang gliders of his own design in a series of flight experiments. He controlled his gliders strictly through

weight-shift, which worked well until he could not recover from a gust of wind on his last flight.

Not all aeronauts relied on weight-shift for control, of course. Some glider-builders in the nineteenth

century employed wing warping as a primitive tool to achieve roll motion. As a result of their own

experimentation, the Wright brothers concluded that control in all three axes was essential if an airplane was

to be truly useful, a philosophy harking back to Cayley. The brothers settled on two parallel planes mounted

horizontally in front of their wings for pitch control; another pair mounted vertically behind the wings for

yaw control; roll control came from warping the trailing edge of the wings through wires attached to a hip

cradle on which the pilot lay. In flight the pilot could control all three, as they were linked.

2

 C.H. Gibbs-Smith. Sir George Cayley’s Aeronautics. London: Her Majesty’s Stationery Office, 1962. 3-10.
5

A NASA F-15 banking over Edwards Air Force Base. The image has been modified to illustrate the loss of a

wing, in order to demonstrate the IFCS’s ability to reconfigure an aircraft and retain controllability. Dr. James

Stewart used this image in an early briefing with the Air Force in explaining the potential advantages of selfrepairing flight control systems.

NASA Photo Illustration EC88 203-6
6

Capable though it was, wing warping in airplanes did not give the fastest control response possible.

Furthermore, the Wrights maintained that they owned a patent on this aircraft control method, embroiling

subsequent designers in real and potential litigation. Some, such as the team lead by Alexander Graham Bell

and Glenn Curtiss, shifted to surfaces either positioned between the wings of a biplane, or located on the

trailing edge of the wing itself – known as ailerons. By the First World War, most airplanes integrated

ailerons into the wings for roll control; elevators embedded in the horizontal stabilizer producing pitch, and a

rudder in the vertical stabilizer-generating yaw. The latter two surfaces could even make use of the prop blast

acting on the cruciform tail to increase control authority.

But as airplanes became faster and more maneuverable, control surfaces had increasing difficulty keeping

up with the common needs of desired flight control. For one thing, the prop wash was no longer a sure

control augmentator. And so, over time control surfaces grew in size to meet these needs. Newer fighter jets

with conventional control systems often incorporated all-moving stabilators (ironically, like the Wrights) and big

ailerons in order to achieve desired control and response. The enlarged control surfaces were a compromise, for

even if they were too large for normal control, they enabled more authority during maneuvers.

In time, and given the “excess” flight surface area, some wondered whether a fighter could be

reconfigured to fly with a reduced number of surfaces and yet maintain control authority. An advanced

aircraft with technologies such as thrust vectoring, canards, and variable-geometry inlets presented even

greater possibilities. NASA engineers working on a commercial version of these smart control systems

realized that on large aircraft similar to the new Boeing C-17, nearly 30 individual surfaces might contribute

to control.3 But they also recognized that a human would have great difficulty controlling all 30 individual

surfaces simultaneously because of the complexity. How then accomplish this?

The Evolution of Flight Controllers

At first, when airplanes were unstable, the brain of the pilot constituted the active control system that

received and transmitted impulses to move an aircraft’s surfaces. During the 1920s, and for many of the

decades that followed, most airplanes were designed to be statically stable to capitalize on the advances in the

ranges and cruising speeds of both civilian and military types. The advantage of a statically stable aircraft is

that it is reluctant to depart from its flight path, sometimes requiring considerable force to do so. Yet static

stability can be a drawback in aircraft meant to suddenly depart a given attitude – a fighter, for example.

By the 1950s, and with the increasing complexity and speed of aircraft, designers realized that one way to

achieve greater maneuverability was the use of what came to be called a “fly-by-wire” system of flight

3

 Jerry Henry. Interviewed at Dryden Flight Research Center, 10 April 2001.
7

control. This entailed using computers—either analog or digital—as the heart of a flight control system. In this

scenario a computer receives data from sensors as well as the pilot, compares the two streams of data, and

commands the control surfaces to move so as to make the airplane meet the pilot’s desires.4

The important nugget here is that with this new computer-controlled flight system an airplane could be

purposefully unstable, yet safe and flyable, because it remained under the active control of a computer in the

loop, rather than just the much-slower-to-react pilot. The designers then realized that reducing aircraft stability

requirements could bring with it a reduction in the size of control surfaces, saving weight. Now, for instance, the

horizontal stabilizer no longer had to be canted in such a way that it provided a downward force to balance the

airplane around its center of gravity: it could be a lifting surface, and so, much smaller.

Fly-by-wire technology is important for understanding the two projects discussed in this book, for neither

project would have been possible without a fly-by-wire system, and in particular, a digital one. The engineers in

this story accomplished all their goals through software, which requires a digital computer and its associated

memory for storage and execution.

For the groups that wanted to experiment with reconfiguration schemes, the few candidates for

experimentation were airplanes initially designed as statically stable, and were only later modified to use a flyby-wire system. The first of these built was the Canadian CF-105 Arrow, a large interceptor with delta wings

and fitted with analog computers at the beginning of its test program. But it was unavailable, as well as

unusable to Americans because of the type of computer, on top of which the Arrow program itself was cancelled

in 1959 after only five aircraft had been built and sixty-six flights made.5

The U. S. Air Force and NASA eventually turned to domestic aircraft, retrofitting configured test fighter

aircraft with fly-by-wire systems. The Air Force used analog processors in an F-4 while NASA installed digital

computers in an F-8.6 Both first flew with a fly-by-wire control system in spring of 1972.7 In the late 1970s,

NASA’s Langley Research Center used its aircraft to conduct a series of experiments on reconfiguring the

sensor suite after simulated failures.8

4

 James E. Tomayko. Computers Take Flight. Washington, D.C.: NASA History Office, NASA-SP-2000-4224.

5

 Richard Organ, et al. Avro Arrow (Erin, Ontario: The Boston Mills Press, 1980).

6

 James E. Tomayko. “Blind Faith: The United States Air Force and the Development of Fly-By-Wire Technology,” Technology and

the Air Force. Washington DC: U. S. Air Force, 1997.

7

 Tomayko. Computers Take Flight.

8

 Ibid., 118-120.
8

The presence of digital (thus reprogrammable) computers on a NASA F-15 made it an ideal platform to

test reconfiguration control concepts. For future aircraft, the use of neural networks and a more benign

operating environment may be acceptable for experiments with a designed-from-the-start fly-by-wire system,

such as that on the C-17. This marriage would take advantage of size and weight savings. But the type of

reconfiguration that eventually became the SRFCS required that high acceleration maneuvering be

maintained even with damage. This was necessary to preserve the maximum ability to continue the fight and

complete a mission even with part of the controls disabled. Such high performance needed excess command

authority to maintain the maneuver margin.9

Previous Self-Repairing Systems

As early as the 1960s NASA initiated research on self-repairing digital systems. While most of the work

in recent years has been directed at solving the problems of battle or natural damage as it affects reliability,

previous research addressed the problem of reliability from the viewpoint of a given system’s longevity.

System reliability and longevity remain an issue, but an airplane has only to be able to return to base or an

emergency field without crashing to achieve the goal. NASA, by contrast, also needed to consider the

possibility of decades-long space missions with no chance of return for repair. With this concern in mind,

NASA’s Jet Propulsion Laboratory (JPL) funded Algirdas Avizienis of the University of California at Los

Angeles (UCLA) to study digital equipment capable of extended systems reliability and longevity.

Avizienis approached the matter from the perspective of the period. In 1961, when he began working on

the project, the most frequent source of systems failure lay in hardware, not software. Furthermore, studies of

piloted programs up to that time suggested that the most common source of future failures would be in the

avionics systems of the computers. Then as now, the chief measure of hardware reliability is Mean Time

Between Failure (MTBF). Avizienis and others working on the problem reasoned that the MTBF “clock” did

not start until the hardware powered up. Therefore, electrical power could be saved and life expectancy

extended among redundant components by only using the minimum hardware necessary for guidance,

navigation, and attitude control, and shedding failed components from the power source while turning on

quiescent ones. He called this assembly of components the Self-Testing and Repair computer, or STAR.10

9 James F. Stewart and Thomas L. Shuck. “Flight-Testing of the Self-Repairing Flight Control System Using the F-15 Highly Integrated

Digital Electronic Control Flight Research Facility,” Technical Memorandum, NASA-TM-101725. Edwards, CA: NASA Dryden Flight

Research Center, 1990.

10 Avizienis, et al. “The STAR (Self-Testing and Repairing) Computer: An Investigation of the Theory and Practice of Fault-Tolerant

Computer Design,” IEEE Trans. Comput., 1971, 1314-1320.
9

The STAR never actually flew, perhaps because critics commented on the single-point failure characteristics

inherent in each component’s switch. However, the concepts of turning off an unneeded redundancy, then

turning it on during critical mission phases, and of cross strapping components, did make their way onto

JPL’s deep space probes.

IBM performed its own analysis of self-repair in the 1960s. Through mathematical modeling it found

that the use of spare components was not optimal. Instead, said IBM, it would be more effective to use

redundant circuits and status registers. In that way switches could be made of Triple Modular Redundant

(TMR) circuits, like those eventually used in IBM’s Saturn Launch Vehicle computer. This system matched

the model posited by the eminent mathematician John von Neumann, in “Probabilistic Logic and the

Synthesis of Reliable Organs from Unreliable Components.”11 In this paper von Neumann suggested that

unreliable components could be made reliable by constructing them with redundant wires, and using a

“majority organ” at regular intervals to vote on the correctness of the circuits. Nevertheless, JPL rejected

this model for its spacecraft, possibly because such redundancies are powered continuously, and draw

precious energy from the supply source.

As electronic components improved in reliability and longevity through the 1960s and early 1970s, both

the Air Force and NASA continued to explore digital fly-by-wire aircraft control. A digital flight control

system, such as that developed and tested by NASA on a converted Navy F-8, provided the flexibility to

conduct several experiments simply by changing the software. Indeed, some of the experiments would have

been impossible without digital flight controls. In all of this, airplanes share with spacecraft issues of size,

power, and weight. Both are limited by their propulsion systems. Airplanes, however, are more easily fitted

with bigger engines to accommodate fully powered redundancies. And by the 1970s full redundancy (all

components powered up) seemed to many a likely trend in new, sophisticated aircraft.

In 1976 the Dryden Flight Research Center acquired a unique F-15 from the Air Force. Most F-15s at

the time had a mechanical flight control system, supplemented by an analog Control Augmentation System

(CAS). On this particular airplane a digital CAS, which could be reprogrammed, replaced the analog CAS.

An analog flight computer requires physically modifying the hardware to achieve new control parameters,

whereas the digital flight computer merely needs to be reprogrammed. In addition to fly-by-wire in the CAS

mode, this airplane served as a test-bed for digital engine controllers. Named HIDEC (Highly Integrated

11 John Von Neumann, “Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components, ” in William

Aspray and Arthur Burks, Papers of John von Neumann on Computers and Computing Theory (Cambridge, MA: Charles Babbage

Institute Reprint Series for the History of Computing, V. 1, The MIT Press, 1987), 553-576.
10

NASA number 802, an ex-Navy F-8, was the dedicated test bed for the digital flight control experiments. Using

the on-board computer from the Apollo 15 command module, this aircraft demonstrated the possibility of digital

fly-by-wire, an essential step in the process of developing IFCS. The aircraft is now on display at NASA Dryden

Flight Research Center at Edwards Air Force Base.

NASA Photo ECN 3276
11

Digital Electronic Control), the F-15 performed numerous integrated flight propulsion control projects. Its

digital engine controls had the capability to share information with the flight control computers and the inlet

controller, improving both response and efficiency. Ultimately, this versatile aircraft set the stage for

experimenting with the concept of self-repairing capabilities on airplanes.
12

Early in the 1980s, the U. S. Air Force’s Wright Aeronautical Laboratory (at Wright-Patterson Air Force

Base in Dayton, Ohio), apportioned some of its “63” (advanced development) funding to investigate the

problems of automatic maintenance and the self-repair of aircraft flight control systems.12 It also designated

some of this money to fund projects of a professor at Wright State University, and some master’s degree

students at the Air Force Institute of Technology. 13 Some of them even built and flew an unpiloted version

of a self-repairing system using the computer language Ada.14 But the bulk of the money went to what was

then General Electric’s Aircraft Control Systems Development operation, a division with a long track record

in the flight control business. The overall objective was to explore the feasibility of self-repair through the

software on a digital computer, and the Air Force gave the project the acronym SR/DFCS, for the SelfRepairing Digital Flight Control System.15

The Air Force had two major interests in the project: maintenance diagnostics, and self-repairing flight

control. Up to half of the squawks (flight anomalies) reported by tactical pilots were “not repeatable,” or

transient in nature, dubbed CND, or “could not duplicate,” by Air Force maintenance crews. Attempts to

duplicate them on the ground often failed, sapping time and money from maintenance programs, yet their

potential importance meant these squawks could not be ignored. Inability to duplicate these anomalies on the

Section 2: The Self-Repairing

Flight Control System

12 Robert Quaglieri. Interviewed via telephone from Air Force Research Laboratory, Air Vehicle Directorate, Wright-Patterson Air

Force Base, OH, by the author, 16 May 2001.

13 Kuldip S. Rattan. “Evaluation of Control Mixer Concept for Reconfiguration of Flight Control System,” IEEE NAECON

Proceedings, May 1985, 560 – 569.

14 S. Pruett Mears and J. Houtz. “URV Flight Test of an Ada Implemented Self-Repairing Flight Control System.” Dayton, OH:

Wright Laboratory, August 1992, WL-TTR-92-3101.

15 J.M. Stifel, C. J. Dittmar, and M.F. Zampi. “Self-Repairing Digital Flight Control System Study,” Final Report for Period January

1980-October 1987, AFWAL-TR-88-3007, May 1988, 1.
13

ground is, in retrospect, no real surprise, since a test run at one G (acceleration equal to the force of gravity at

sea level) differs considerably from operations at six Gs in the air. Fixing these deficiencies and the

maintenance diagnostics system was thought of as an experience-leveler. The Air Force hoped, among other

things, that the SR/DFCS program would lead to a reduction of false alarms when fielding the next

generation Advanced Tactical Fighter (ATF). Additionally, the automatic diagnostic system would allow

reports of the anomalies to be sent ahead, enabling ground crews to address them shortly after landing. This

idea became closely allied with the concept of in-flight self-repair, since the Air Force hoped the self-repair

capability would function in response to a malfunction or battle damage. The Air Force decided early in the

program that its approach would be a robust form of reconfiguration. For instance, if an aircraft lost a

stabilator, control would not fall to just one or two other surfaces operating at brute force to compensate.

Rather, the system would configure the remaining flight control surfaces to behave in a blended fashion,

enabling the airplane to continue flying, albeit with reduced capability. Further, if a surface retained even

partial capability, it, too, would be utilized rather than deleting it from the control suite.

GE realized the problem had two parts: detection and reconfiguration. For the former, GE engineers

developed a System Impairment Detection and Classification (SIDC) box. The SIDC used differential

accelerations to determine if a computer model of the airplane was acting strangely: variations in aircraft

accelerations were judged to result from failed or missing control surfaces. Once the SIDC did its work, the

Effectors Gain Estimation (EGE) part of the system calculated the differences in the electrical gains

commanding the various surfaces. Finally, these gains were fed into the Reconfigurable Control Mixer

(RCM), or “mixer,” where they were combined with, and then adjusted, the commands to the control

surfaces. Additionally, the SYSDYN (SYStem DYNamics) module contained a mathematical model of the

unimpaired aircraft. The performance models of both the unimpaired and impaired aircraft were used as

inputs to the other modules.16 This system’s architecture remained essentially the same, allowing GE to adapt

it to various aircraft.

The Air Force initially targeted the AFTI (Advanced Fighter Technology Integration) F-16 as the aircraft

on which to experiment. The AFTI F-16 had the normal stabilators, rudders, and ailerons of most tactical

aircraft, but it also had a set of canards. The RCM could then command various combinations of these

surfaces.17 For example, the AFTI might use a combination of the stabilators, flaps, and canards for pitch

16 John H. Corvin, William J. Havern, Stephen E. Hoy, Kevin F. Norat, James M. Urnes, and Edward A. Wells. “Self-Repairing Flight

Control System, Volume I: Flight Test Evaluation on an F-15 Aircraft,” Final Report for Period October 1987 – December 1990,

WL-TR-91-3025, Volume I, Part I, August 1991, 3-11.
14

18 Stifel, Dittmar, and Zampi. “Self-Repairing Digital Flight Control System Study”, 20.

control; the same surfaces applied differentially, plus the rudder to achieve roll; or the rudder plus differential

canards to generate yaw.18 But when the AFTI aircraft was not immediately available, they adapted the system

instead to the Large Amplitude Multimode Aerospace Research Simulator (LAMARS) at Wright-Patterson.

LAMARS is a full motion simulator with pilot-in-the-loop capability, used to simulate the characteristics of

the AFTI F-16. The Air Force ran the tests in November 1986, during which the SRFCS performed quite well

in the LAMARS, encouraging continuation of the program.19

The project managers at Wright-Patterson thought that actual flight tests would be required to achieve

sufficient levels of proof-of-concept.20 Accordingly, Air Force Wright Aeronautical Laboratories named

Robert Quaglieri as project leader. As early as 1985 some of the spin-off work generated under the Air Force

funding became available in the form of papers, theses, and the personal knowledge of students. Aware of

this, GE brought in Alphatech to help design the software for the project.21 Now all the program lacked was a

suitable aircraft. Quaglieri and the other Air Force personnel (Lt. Robert Eslinger, Phillip Chandler and John

Davison) formulated an approach for flight test of the SRFCS following discussions with James Stewart at

NASA Dryden. The plan was to deploy the SRFCS on a NASA research aircraft managed by Stewart.

In 1976, NASA acquired use of airplane 71-0287, the eighth pre-production F-15, and designated it

NASA 835. Based at Dryden Flight Research Center, it differed from production F-15s in its control system

arrangement. Line aircraft had mechanical controls with an analog Control Augmentation System (CAS).

NASA replaced the CAS in 835 with a digital flight controller programmed in about 28,000 words of Pascal.22

17 Quaglieri, interview.

19 Ibid. , 81.

20 Quaglieri, interview.

21 Corvin, William J. Havern, Stephen E. Hoy, Kevin F. Norat, James M. Urnes, and Edward A. Wells. “Self-Repairing Flight Control

System, Volume I: Flight Test Evaluation on an F-15 Aircraft,” Part II, p. 3. Thanks to Dr. James F. Aldridge of the Aeronautical

Systems Center History Office for explaining the designations of the Air Force Wright Aeronautical Laboratories over the last

quarter century.

22 Corvin et al,”Self-Repairing Flight Control System, Volume I Flight Test Evaluation on an F-15 Aircraft,” Volume I, Part I, August

1991, Part I, 5-22.
15

A diagram of the Intelligent Flight Control System designed to make a partially stricken aircraft flyable .

NASA EC89-117-9
16

had also briefed the Air Force on the F-15 HIDEC capabilities. And so the Air Force contacted him about the

availability of the F-15 HIDEC aircraft, with tests of the SRFCS in mind. After several meetings to evaluate

the capabilities of the aircraft and determine how to integrate the multiple research activities on the aircraft,

the Air Force selected the F-15 testbed to prove the new technology. NASA then assumed the role of “prime

contractor,” obtaining the flight control hardware from General Electric, the software from McDonnell

Douglas Corporation in St. Louis, (which had 12 engineers on the project, led by James Urnes Sr.), and

the maintenance diagnostics from the Air Force. Stewart of Dryden became the project manager for the

SRFC project at NASA, while at the same time continuing to be project manager for all of NASA F-15

HIDEC research activities.23

In addition to control computers, engineers added a Rolm Hawk computer to make the airplane a more

robust test-bed. With the addition of the Hawk and its added capability, Stewart initiated the Performance

Seeking Control (PSC) project, a NASA investigation that developed in parallel with the SRFCS. The PSC

project was an adaptive on-board optimization of the total propulsion system. The single engine phase was

completed in 1990 and the Dual Engine Phase completed by Oct. 1993.

The Hawk could execute 2.5 million instructions per second and had a memory of two million words,

making it both faster and larger than any air- or space-borne machine. Even so, the SRFCS and maintenance

diagnostics stretched its speed capabilities. The computer’s memory, though comparatively large, limited the

SFRCS software, which resided almost entirely on the Hawk. The software suffered from such failures as

locked trim and some other locked control surfaces, but only one surface—the right stabilator—could

simulate losses, and then only at increments of 50, 80, and 100 percent, depending on the circumstances.

So that the on-going ATF program might benefit from the results of SRFCS flight research, managers

began the SRFCS flight test earlier than planned. As a consequence, there was a Phase 0 flown in March

1989, in which the maintenance diagnostics were successful tested in flight. The maintenance diagnostics and

the SRFCS occupied 312,000 bytes in the Hawk, very spare by current standards, leaving about 28 kilobytes

By the 1980’s this F-15, with its unique capabilities, became the platform for digital engine control and

integrated flight propulsion control research. Overall research activities were led by NASA and partnered to

various degrees with the Air Force.

Stewart, the F-15 HIDEC project manager, had been in close contact with the Air Force regarding both the

current NASA research and the proposed performance seeking control (PSC) flight propulsion research; he

23 Most of the integrated flight propulsion control research performance with the F-15 HIDEC aircraft, including the SRFCS, is

summarized in NASA Technical Memorandum 4394 (1992) by James F. Stewart entitled Integrated Flight Propulsion Control

Research Results using the NASA F-15 HIDEC Flight Research Facility.
17

The Rolm Hawk computer which served initially as the primary on-board computer on the F-15 HIDEC flying

test bed. Capable of 2.5 million instructions per second it was the most advanced airborne computer of its time.

NASA Photo EC88 018-9
18

of flight control software available. Lear Siegler and Alphatech performed some modifications to the

software in 1989 following on the Phase 0 testing. So adaptable was the Rolm Hawk in this

arrangement that other projects used the same F-15, since its programmable computers permitted a

great amount of flexibility. Indeed, within a decade, when the Air Force began converting the F-15 to

test-fly the maintenance diagnostics and the self-repairing system, it had already undertaken nearly

500 flights for NASA and been involved in some 25 projects.

NASA 835 required modifications before it could be fully useful to the Air Force for control reconfiguration.

The ailerons, for instance, retained a fully mechanical set of actuators. For the SRFCS ‘s tests NASA Dryden’s

Wilton Lock and his team replaced them with a set of electrical actuators usable by the new system.24 McDonnell

Douglas designed and built the electronics to control these surfaces, and in the end NASA 835 became the only

F-15 to have electronic roll control.

During LAMARS-simulator testing of the self-repairing system, engineers discovered that if pilots were

not given visual cues of the new maneuver limits following reconfiguration, they would stray from

controlled flight. They found that the system was optimal at 0.7 Mach and 20,000 feet altitude, and so

NASA installed the Positive Pilot Alert system. This display projected information in such a way that the

pilot could see it without tilting his head and losing sight of what was happening outside the cockpit. It

drew a rectangle on the head-up display showing the pilot the new limited maneuvering envelope after selfrepair. All would be fine, provided the pilot stayed within the new parameters.

The Air Force took a novel approach to this program. Essentially, it treated NASA as the lead, allowing

NASA to manage the project and provide a flight test program. In turn, NASA contracted the electronic systems

integration and systems test task to McDonnell Douglas Corporation in St. Louis. MDC had the flight simulator

and avionics laboratories that would prove critical to the success of this very complex control system. It also had

controls engineers experienced in the flight dynamics and control software so necessary to fly the system in

NASA’s F-15. To accomplish its share of the program MDC formed a team of 12 for the SRFCS project.

This arrangement enabled programmatic flexibility. When delays in other programs caused engines to

be delivered late, NASA research pilots simply flew another self-repairing flight. By this expedient, NASA

provided more flights and flight hours than initially promised, with no cost overrun. In fact, Stewart

finished the project $4,000 under budget, and for a multi-million dollar project to be on budget, it was an

outstanding accomplishment.

24 Wilt Lock. Interviewed by author, Dryden Flight Research Center, 6 April 2001.
19

The port wing of the F-15 HIDEC, opened so that technicians could replace mechanical actuators with electric

servos. This was part of the process of fully integrating the Rolm Hawk computer en route to making the first

IFCS flight.

NASA Photo EC88 249-5
20

The SRFCS activity fell under a broad memorandum of understanding between Wright Aeronautical

Laboratories and Dryden Flight Research Center signed in 1985.25 It represented an extraordinary example

of two of the government’s leading research centers cooperating to test a revolutionary concept, and it

proved for both to be one of their closest collaborations. By 1989, the Wright Laboratory team consisted of

John M. Perdzock, Program Manager; Robert Yeager, Flight Test Director; and Capt. Barry Migyanko, the

project engineer. NASA’s Wilt Lock was the operations/systems engineer, Thomas Shuck of NASA the

test engineer, and Stewart led the team as project manager. NASA assumed the role of Responsible Test

Organization (RTO).26

As the flight research approached, the Air Force side expressed a sense of urgency. The demonstration

deadline for new technologies set for inclusion in the ATF stood near the end of 1989. Still harboring some

hope of incorporating the SRFCS into the ATF, the Air Force pressured NASA to fly before year’s end.27

Flight number 555 of NASA’s F-15, tail number 835, became the first flight of the SRFCS, taking place

on 12 December 1989. During the 1.7-hour flight in a bright blue high-desert sky over Edwards, pilot Jim

Smolka ran the SRFCS off-line to check its operation. He then refueled from a tanker and brought the

SRFCS on-line and locked it at an impairment of the trim. The envelope limitations for the SRFCS were

15,000 to 25,000 feet altitude, -5 to +15 degrees angle of attack, and a speed of 0.5 to 0.9 Mach, and the

SRFCS handled the trim failure quite well within those limits.

The next day Smolka again flew the F-15, this time to test battle damage. The SRFCS handled

satisfactorily with no impairments, even when subjected to a 50-percent right stabilator loss, followed by an

80-percent right stabilator loss. Toward the end of the 1.2-hour flight the system uncoupled with an incorrect

SIDC detection. Engineers later determined the probable cause to have been incorrect weight figures

programmed into the Hawk, accompanied by sideslip angles and roll rates outside the operating range.

On 18 December, the problem persisted. When research pilot Bill Dana activated the SRFCS, it

once again handled stabilator impairments, including locking at an angle, but the system uncoupled at

high roll rates. Engineers again found an improper weight/mass calculation, and corrected it before the

26 Stifel, Dittmar, and Zampi. “Self-Repairing Digital Flight Control System Study,” Final Report for Period January 1980-October

1987, AFWAL-TR-88-3007, May 1988.

25 Monthly Project Status Reports, to the Director of Flight Operations from Chief, Aeronautics Projects Office, February 1988.

27 Quaglieri, interview.
21

NASA F-15 tail number 835, the aircraft dedicated to the HIDEC program, preparing for its first flight in 1989. Used

early in the IFCS and SRFCS project, this aircraft was eventually replaced by the F-15B acquired from the Air Force,

an airframe that itself had been extensively modified.

NASA Photo EC90 245-2
22

next flight. To the satisfaction of many, five maintenance diagnostic scenarios run during the 1.3-hour

flight that day produced the correct messages on the Positive Pilot Alerts display on the Head-Up

Display (HUD).

Up to this point flights had only tried to check out the SIDC. The fourth flight involved maintenance

diagnostics and all the components of the SRFCS architecture: the SIDC, the EGE, and the Mixer. Once

again the maintenance diagnostics scenarios worked perfectly, giving correct warnings to pilot Tom

McMurtry on the HUD. The SIDC also repeated its past performance, doing well on partial stabilator losses,

yet faltering on lateral maneuvers. The Mixer worked well with partial surface losses and locked trim, while

the EGE transmitted values of only 0 and 100 percent. This last flight of 1989 lasted 1.2 hours, totaling 5.4

hours of test flights for the month. The EGE transmitted values of only 0 and 100 percent. In the end it is

difficult to tell whether the results influenced the ATF program, especially considering the mixed results, for

the ATF does not include a full SRFCS and maintenance diagnostics system. The fundamental principles

from both systems did eventually find their way into the ATF.

Three NASA research pilots flew the F-15 during the SRFCS series. They were: Jim Smolka, then new to

NASA and eventually the Chief Pilot at Dryden (after 31 July 2000); Tom McMurtry, the principal pilot of

the F-8 supercritical wing, who later went on to become Chief Pilot, and then Director of Flight Operations;

and Bill Dana, an X-15 pilot who flew the hypersonic plane’s 199th and last mission as well as the lifting

bodies, and who eventually became Dryden’s Chief Pilot, Assistant Chief of Flight Operations, and Chief

Engineer.

The first flight of the new year, on 10 January 1990, was meant to be a further checkout of the Mixer. An

engine had been swapped during the three-week down time, meaning that it, too, had to be tested. The engine

performed well in the 1.5-hour flight, and Smolka tested the Mixer by checking its performance with the

aircraft unreconfigured, and with a surface locked or missing. The Mixer performed well with the control

surface locked at two- and four-degrees but tended to balk when set at six degrees.

Flight six of the SRFCS came two days later. The maintenance diagnostics, already a success, had another

flawless performance on the two-hour flight. There were two sets of maneuvers planned to test the Mixer. The first

set, Block A, included some partially missing surfaces and stick doublets of various kinds. Block B, the second set,

included wind-up turns, sideslips, pitch capture and tracking. In this instance, the performance of the Mixer proved

to be spotty. The system showed handling improvement during some maneuvers between this flight and previous

ones, while some worsened. Yet, Dana reported improved performance over the un-reconfigured aircraft.

The program managers planned three missions to further test the gain adjustment component, the EGE,

but the first ended after only half an hour because of uncommanded fuel venting. The second flight, with
23

McMurtry at the controls again, lasted only an hour of the planned 1.5 hours, due to unsatisfactory SIDC

performance and an out-of-tune EGE. The final flight of this set, with Dana in the cockpit, lasted the full 1.5

hours, but again the EGE showed no improvement, and the SIDC was less than perfect. This flight had also

been delayed by software errors, which crashed the Rolm Hawk computer.

The following flight on 31 January, the 10th of the SRFCS program and the 564th of NASA 835, lasted

only 1.3 hours. Intended just for evaluation of general flying qualities, the flight experienced a takeoff delay

due to the corruption of the non-volatile memory on power up. This, in turn, made the airplane miss the

rendezvous with the tanker, and so Smolka flew a shortened flight plan with just a few impairments. Even

so, the SIDC and Mixer worked well together. (Many of the test flights at Edwards required aerial refueling

in order to extend the relatively short flying time of the test aircraft, which is usually a fighter.)

The next two flights made up for the previous abbreviated one. On 2 February, McMurtry flew 2.7

hours, the longest flight of the F-15 in five years, during which he tested the SIDC and EGE. The former

showed no improvement over the baseline, which analysis attributed to pilot error, but the latter showed

some improvement. Five days later flight 12 (number 566), with Dana in the cockpit, broke the recent longevity

record. He tested all three components of the General Electric architecture during the 2.8-hour flight. The SIDC

and EGE continued their abysmal performance, neither matching even the baseline, while he also tested the

Mixer on large maneuvers.

On 9 February, during flight number 14 with Smolka in command, the SIDC and EGE worked together

well on the first series of simulated impairments. Engineers later hypothesized that the EGE worked simply

because, when he activated it the residual data was at its best. A large amount of Mixer data was collected

on the 1.4-hour flight. When the SIDC alone was tested, it returned passable results only once in three tries.

Program managers scheduled a two-hour flight, with McMurtry at the controls, for 12 February 1990,

allowing enough time to run another maintenance diagnostics scenario. The test failed, even while several

reconfigurations with surfaces locked at various angles performed correctly. The next day, Smolka repeated

the maintenance diagnostics scenario, this time successfully. The EGE used the SIDC to detect failures and

transmitted a couple of gain sets, which did not, however, work. The reminder of Smolka’s 1.9-hour flight

included tests at off-nominal points within the edge of the envelope.

During the 23 February test flight lasting 1.9 hours, the SIDC detected only 25 percent of the simulated

failures. Dana flew both Block A and Block B maneuvers during which there was excessive pitching

motion; but he had opened the speed brake during some maneuvers, which was not part of the model.

On 28 February, McMurtry tried acceleration and decelerations with impairments. The 1.8-hour flight

also tested the edge of the envelope at 16,000 and 24,000 feet. In addition, McDonnell Douglas, the
24

manufacturer, wanted some cross-coupling data, which McMurtry obtained by trimming the aircraft in a

turn, and then increasing Gs. The flight on 2 March aborted because of a failure to align the inertial navigation

system (INS). By the time the INS was ready, clouds had moved into the box marking the edge of the envelope.

On 6 March 1990, Dana tested the system for general flying qualities. His only complaint during the

1.7-hour flight: some pitch bobbing caused by the Mixer during tracking. Engineers traced the pitch

bobbing to the very small time delay in the reconfiguration feedback correction commands coming from

the Hawk flight processor. This small delay caused the motion of the plane to trail the pilot’s command,

bracketing the intended tracking error, and making it extremely difficult to center the sight exactly on the

target airplane. Due to the size of the SRFCS software, the Hawk could only operate at 20 Hz or 20

updates per second, whereas the preferred rate for precision flight control is updates at 80 times per

second. The next day, a 2.3 hour flight by McMurtry again resulted in pitch bobbing, and McMurtry

adjusted to it. This series ended with a 2.4-hour flight in which he ran cross-coupling tests, and attempts to

expand the SIDC and EGE envelope. But the flight was halted, even though fuel remained in the tanks,

when Smolka became ill from too many 360-degree rolls.

One fact emerged during these flights, vividly demonstrating the trust that the pilots had in the SRFCS

flight controller, as well as its performance. When the pilot rolled the reconfigured aircraft more than 200

degrees/second, the system disengaged and returned control back to the F-15’s normal flight controller,

with no false damage imposed on the controls. The roll disengage limit was purposely programmed into

the SFRCS software by McDonnell Douglas; and at the request of safety engineers, placed in the software

as a permanent value that could not be changed. Both McDonnell Douglas and NASA firmly believed that

no pilot would ever try to roll a damaged aircraft at 200 degrees/sec, a value on the maximum edge of

performance of the F-15, and something comparable to driving a family sedan over 100 mph. Yet the pilots

complained loudly to McDonnell Douglas about this limit, wanting an even higher roll rate set in the

system. This demand by the NASA pilots astounded the engineers, and illustrated the confidence the pilots

had in the SRFCS.

Two weeks passed before the next test, an ambitious 1.3-hour flight that tried pitch doublets at both

one and four Gs, and several maneuvers aimed at cross-coupling the controls. With the SIDC and EGE

stubbed out, a maintenance diagnostic scenario ran with Mixer only. Dana finished the day’s test plan with

some large amplitude maneuvers, including 360-degree rolls.

The flight two days later was supposed to test most of the same items with a different pilot, but Dana

flew both missions since no one else was available. He put another 1.3 hours on the aircraft and tried all
25

five maintenance diagnostic scenarios. Although the SIDC did not pick up on the 50-percent surface loss

in one of the failure scenarios, everything else was a success. Dana flew some propulsion-only maneuvers

as preliminary work on a later program. The tanker aborted its flight, missing the rendezvous, so he cut the

test short, and returned to base.

About this time, Stewart and Bill Burcham of NASA both suggested using the two engines of the F-15

for propulsion-only control in a project entirely separate from SRFCS. This was partly inspired by a

United Airlines flight in 1989 that lost hydraulics in the tail after an uncontained compressor failure in the

tail-mounted engine of a DC-10. As the crew struggled to maintain control of the stricken aircraft, a

deadheading pilot came to the cockpit and assisted the crew by using the throttles to control the plane with

differential thrust of the wing-mounted engines. In spite of the resulting crash on landing at the Sioux City,

Iowa, airport, nearly two thirds of those on board survived. Burcham and Stewart felt that even though the

F-15 had its two engines much closer together than the DC-10, the jet’s excess power would still generate

sufficient differential thrust for experimentation. The engineers traveled to St. Louis to discuss with

McDonnell Douglas such a development using NASA 837, another F-15 in the inventory, as a test vehicle.

On the way, Burcham drew a diagram of his idea on a TWA paper napkin. At McDonnell Douglas they

met with controls engineer Urnes Sr. Urnes himself had experience with Navy carrier landings using an

experimental autopilot that employed thrust changes to control landing approach attitude, and McDonnell

Douglas agreed to study the concept, conducting a feasibility study using their F-15 simulator. Most senior

flight controls engineers at McDonnell Douglas doubted that a highly responsive fighter like the F-15

could ever be landed without any active flight control, since in modern fighters the control system

stabilizing feedbacks are dominant for every maneuver the pilot desires to make. Thrust changes without

controls would be very slow, leading to uncontrollable pilot induced oscillations (PIO), according to all the

available data on pilot handling qualities requirements. Roll and yaw control would be even more difficult,

with no rudder force to dampen yaw motion, resulting in a slow spiral to the ground.

Despite the contractor’s misgivings, Stewart, Burcham, and Urnes Sr., instructed the engineers to

develop control feedback software for the digitally controlled engines to be used on 835, and to install the

design on the large visual display F-15 flight simulator at McDonnell Douglas. Then they invited NASA

test pilot Gordon Fullerton to St. Louis to test it. Fullerton did not like the control stick method of

command, recommending instead two thumbwheel controllers for pitch and roll. McDonnell Douglas

initially rejected his idea, but reconsidered when the Air Force found a quad redundant thumbwheel panel

that could be installed in the F-15. They discovered it in the same McDonnell F-4 test plane that pioneered
26

The legendary “design on a napkin” sketched by Bill Burcham while flying to visit McDonnell Douglas in St.

Louis. Here the Propulsion Control System is laid out with a planned installation on the NASA F-15 HIDEC.

NASA Photo EC94 42805-1
27

fly-by-wire control for the USAF, now located in the Air Museum at Wright Patterson. After much discussion

with USAF Wright Labs test engineer Bob Yeager, the director at the museum agreed to part with the panel

for the test program. Fullerton’s suggestion became the key to success, completely eliminating any pilot

induced oscillation from the system.

Meanwhile, at Dryden the test flights continued. During NASA 835’s flight number 579, McMurtry tried

the maintenance diagnostic scenarios again, which failed at the same place as before. He also performed

numerous SRFCS maneuvers in the 2.3-hour flight, including wake turbulence assessments using the chase

plane’s vortex. The tests also included a simulated landing approach at 10,000 feet and 0.35 Mach, rolling

through 30 and 45 degrees with the trim locked. Two days later, on 28 March 1990, Smolka tried inputting

some new gains to the EGE developed by McDonnell Douglas in St. Louis. The SIDC failed with surface loss

at 80 percent, rendering these new values useless. The 1.9-hour flight ended with another attempt at

propulsion-only control. But the flight ended early for lack of fuel. Although Smolka was scheduled for the

final flight of the program on 30 March, it was aborted because the roll and yaw CAS would not engage.

Analysis revealed several corrupted non-volatile memory locations, although they would have been

inconsequential to the flight. In the end, the ground crew was unable to reproduce the problem, which was,

ironically, one of the very reasons for developing the maintenance diagnostic system.

The final, 1.9 hour flight on 3 April had one of the longest sets of test cards of any SRFCS mission. First,

the video crew tried to get a shot of a longitudinal doublet. Then the pilot tried the 50 percent surface loss that

had stymied the SIDC before. It took four tries, but it finally worked, although the fourth attempt occurred

only because the video team in the chase plane had temporarily run out of tape and asked the pilot to repeat

the maneuver. Later he exercised the SIDC/EGE/Mixer at various fuel weights, altitudes, and speeds. The

final segment tested propulsion-only control again. Laterally, the response was promising, but slow.

Longitudinally, the nose to rose and fell, but once down, the pilot could not raise it. Pitching up and reducing

airspeed caused a sink rate, but the pitch angle did not decrease: instead, the angle of attack increased. Under

these conditions pitch was not reversible in either direction.

The 25 flights of the SFRCS and maintenance diagnostics ended with general success on the maintenance

side, understandable but frequent failures on the SRFCS side, and with early success of propulsion-only

control. Yet, some uncertainties persisted about the results of the SRFCS investigation. The maintenance

diagnostics project seemed the most successful. It had only one fault in the several flights in which it was

28 Corvin, et al, “Self-Repairing Flight Control System, Volume I: Flight Test Evaluation on an F-15 Aircraft,” Volume I, Part I,

August 1991, p. 6-28.
28

exercised. In the SRFCS, the SIDC was right every time it sensed a control surface failure, but it sensed this

only 61 percent of the time.28 The Cooper-Harper ratings by the pilots averaged 2 for the aircraft unimpaired,

4-7 impaired, and 4-5 reconfigured.29 Nevertheless, NASA proved the concept of the Self-Repairing Flight

Control system, while engine-only control emerged as a viable project.

Moreover, NASA eventually demonstrated a successful, if not uneventful, landing of the F-15 using only

the engines. Despite the difficulties of the flight, the technology developed by the NASA team enabled its

pilots to fare better than the United Airlines crew had at Sioux City. Propulsion-only control eventually

became a full-fledged Ames-Dryden initiative using an MD-11, an aircraft with the same engine layout as the

DC-10. It also had the additional advantage of being primarily a commercial aircraft, rather than a military

fighter jet. Follow-on versions of the landing system would use artificial neural nets, which were a central

part of the Intelligent Flight Control System flown on the F-15 flight program, for repair in flight. In 1995

research pilot Gordon Fullerton successfully landed the MD-11 using propulsion-only control.

Both the maintenance diagnostics and self-repairing flight control systems were researched off and on for

another decade.30 The program remained active principally because all U.S. fighters, bombers, and transports

after 1990 were controlled by fly-by-wire systems with digital computers. Wright Laboratory’s Robert

Quaglieri predicted that greater possibilities existed for the application of self-repairing concepts of aircraft

layouts, given the potential for reconfiguration and engine-only-control early in their design cycle, because by

relying on self-repairing and engine-only-control, the redundancy levels inherent in today’s designs might

then be reduced.

Stewart, the NASA program manager, placed much of the credit for the program’s success on the F-15

itself. It was one of the few tactical aircraft available for tests that had a research computer, in addition to

digital flight controls. NASA retired the aircraft in 1992, replacing it with an F-15 that is not only completely

fly-by-wire, but also has canards made out of the horizontal tails of an F-18. This airplane, F-15B number

837, still serves as the test bed for on-going IFCS experiments.

The SRFCS was mostly conventional, inasmuch as it used the controllers in expected ways. It took

several flights to work the bugs out of the system, but the concept was proved without much “out of the box”

innovation. “Innovation” came to the forefront in the follow-on program, which used artificial neural

networks.

29 Ibid., 6-112.

30 Quaglieri, interview.
29

Section 3: The Intelligent

Flight Control System

After the Air Force discontinued its funding of SRFCS, Stewart and other NASA researchers continued

to pursue their interest in self-repairing systems.31 Moreover, while the F-15 used for the SRFCS tests was

retired by the mid 1990’s, its replacement – another F-15 – had some of the same characteristics of the first

airplane, making it an ideal platform for further research on the concept. Stewart had planned advanced control

research using multi axis thrust vectoring, advanced reconfigurable control technologies, as well as a battery of

new tests on this new vehicle.

Since Stewart obtained and equipped this unique F-15 for advanced control technologies research, he

named the project F-15 ACTIVE (Active Control Technology for Integrated Vehicles). NASA number 837 was

equipped with a full authority digital fly-by-wire system, not just a replacement for the CAS. It also

incorporated canards, wings forward of the standard F-15 wings, which could make the aircraft unstable. In

this case, these canards were actually F-18 horizontal stabilizers, and the control system was largely off-the

shelf, and also from the F-18. Moving the canards with angle of attack enabled pilots to re-stabilize the basic

airframe. A Vehicle Management System Computer (VMSC), a Motorola 88100 series processor, augmented

the regular flight computers. The VMSC provided additional fast computing power and, with some two million

words, a larger memory than had been available for previous tests. In addition, a research 68040 processor was

added to each of the four-channel primary flight control computers, giving this research airplane the highest

control processing capability in the industry.

The project obtained support from two other NASA centers (Langley and Glenn) and received assistance

from the USAF, as well as from McDonnell Douglas and Pratt and Whitney. These partners contributed to the

first ACTIVE experiment by integrating Flight Research and Demonstration of Thrust Vectoring Nozzles with

advanced NASA techniques that were later used with the neural networks. Stewart negotiated with USAF for

the aircraft, with P&W for the multi-axis nozzles, and with McDonnell Douglas for the vehicle management

system computer. These contributions cost NASA nothing, and consequently leveraged NASA’s own

31 Stewart, interviewed by the author, 4 April 2001.
30

A top-down view of the F-15B ACTIVE as if flies over the Mojave Desert. This perspective highlights the canards –

not found on production F-15s – which were integral to the SFRCS/IFCS experiments. The canards were in fact

stabilators from an F-18. The jet’s speed brake, just aft of the canopy, has been deployed in this photograph casting a

shadow on the fuselage.

NASA Photo EC96 43780-1
31

contributions, making the project possible. This aircraft remains one of the most heavily instrumented in all of

flight research and, as such, represents an improvement over the HIDEC F-15 NASA test bed.

Following the SRFCS program, Boeing experimented with broadening the reconfiguration process that

would address both “A matrix” and “B matrix” failures so successfully demonstrated on the F-15. “B matrix”

refers to the control surfaces, one of which, the right stabilator, failed during SRFCS flights. Boeing was

interested in the “A matrix” or aircraft airframe damage (such as the loss of a wing, as experienced in the

Israeli F-15 mid-air collision). Boeing had the wind tunnel data from the partly missing right wing, and

attempted to use the SRFCS process in simulation to restore control, but their attempt did not succeed. SRFCS

used a dynamic inverse method to track and correct damage conditions twenty times a second, using the “B

matrix” inverse in this computation, but this inverse process could not be expanded to fit the more complex “A

matrix” type failures.

Still, Boeing sought a process that would deal with “A matrix” and “B matrix” damage situations. It

found that the best way was to continually calculate all the important stability derivatives contained in both

the A and B matrix aircraft definitions, and then, having found these derivatives, apply an advanced adaptive

flight controller to provide the control surface commands. This flight controller would continually solve the

control system gains for the best control response obtainable under the operating conditions of the aircraft,

whether damaged or undamaged. Solving the control gains during flight implied use of a real time Riccati

solver (linear algebraic equations) while finding the critical stability derivatives implied use of self-learning

neural networks, both very challenging tasks. Both Stewart and Urnes Sr. recognized that future IFCS

techniques using Neural Networks would require greater computer capability than existed on the ACTIVE

aircraft.

One other program was key to the success of neural network flight control demonstrations. DARPA

sought a more advanced control concept in which to show off the benefits of fly-by-light. Stewart worked

with DARPA to successfully fund a two-year program through McDonnell Douglas to investigate fly-by-light

aircraft control in a program titled Fly-by-Light Advanced Systems Hardware (FLASH), begun in 1994.

McDonnell Douglas proposed that a neural network Intelligent Control project would be an ideal showcase,

blending highly advanced controls technology into the fly-by-light system. Thus, a subtask was added to the

FLASH program for flight hardware demonstration of such a system. DARPA designated Dan Thompson at

the Air Force’s AFRL to lead the program.

Under the FLASH program, General Electric’s controls division worked to increase computer capacity

in order to match the needs of the IFCS. GE turned to its 68040 processor and supplied subsystems that were

successfully integrated into the testbed. Without the 68040, IFCS flight tests would not have been possible, and the

later NASA programs benefited greatly from DARPA and AFRL’s support during the FLASH program.
32

NASA F-15B ACTIVE in flight. Notable in this photo are the canards that were added to the airframe by the Air

Force, from whom NASA acquired the aircraft. Able to pivot, thereby changing the angle of attack, the canards

were used to blank airflow over a wing, simulating loss of that wing in flight. The SRFCS/IFCS could then

reconfigure control without actually sacrificing an aircraft.

NASA Photo EC96 43415-1
33

Artificial Neural Networks Introduced

Stewart decided almost from the outset to investigate the efficacy of neural networks in creating the

Intelligent Flight Control System (IFCS). In fact, the same McDonnell Douglas Corporation group that had

worked on the SRFCS, still led by Urnes Sr., had already figured out an approach and started work on a pretrained artificial neural net.32 MDC established a neural network laboratory in 1991, and staffed it with

young, innovative engineers with academic backgrounds in artificial intelligence technologies. In 1991,

Urnes Sr., directed a company funded research project to rework the F-15 SRFCS damage adaptive software

incorporating the new neural technology. To the surprise of the MDC researchers, the neural network version

not only provided better accuracy in modeling the F-15 stability properties, it also performed this task with

nearly a 40:1 reduction in software (primarily by eliminating the massive table look-ups required in the

SRFCS). MDC officials then approached NASA with a proposal for flight evaluation of the neural network

software, leading to meetings with Stewart and Terry Putnam. Representing the NASA Headquarters, Putnam

was instrumental in locating funds for the flight research program. NASA Headquarters, which typically

encourages inter-center collaboration, suggested that Stewart contact researchers at NASA Ames already

involved in neural networks.33

During 1992, Stewart – who earned his Ph.D. in both Engineering (Digital Optimal and Adaptive

Control) and Business – met Charles (Chuck) Jorgensen, a NASA scientist and branch chief who came to the

agency from a position with Thomson-CSF, the French avionics firm. Jorgensen earned his doctorate in

mathematical psychology from the University of Colorado in 1973, and his dissertation included some work

with neural networks (See the sidebar for how these nets function on page 35-36). He kept working on them

part-time until arriving at NASA in 1989. There he started the “neuro” laboratory at the Ames Research

Center. During one of Stewart’s visits to Ames, Jorgensen showed him the lab. Stewart was already thinking

of neural nets that could learn in real time as the next logical technology beyond both the SRFCS and the pretrained neural nets of the IFCS; Jorgensen was looking for a suitable aeronautics application, and the two

joined forces.

Stewart and Jorgensen were enthusiastic about neural networks since the technology itself seemed

capable of learning new patterns of behavior. This capability made it possible to reduce the time needed to

develop and test new flight control systems. It also meant that every contingency did not need to be

thoroughly defined ahead of time, making it possible to reduce the number of high cost tests, including wind

32 Urnes. Sr., interviewed by the author, St. Louis, 20 April 2001.

33 Ibid.
34

34 Annual Report, 3/95-7/96, p. 2-1

tunnel runs and even some test flights. A dynamic neural network could adapt to new circumstances. Jorgensen

wanted to use a dynamic learning network, but he understood Stewart’s desire to fly an already trained network

first. It was only in 2001 that Jorgensen’s ideas finally began to be implemented.

When the IFCS made its first flight, it flew with the neural net software operating “open loop,” that is, without

linking to the aircraft control system. The ACTIVE F-15 made these “Phase I” flights in 1996 solely to compare

the output of a pre-trained neural net to the stability properties, or derivatives, of the F-15. Five “Phase 0” prelearning neural net flights were flown in order to generate simulator data, with the conventional flight control

software in the foreground and the neural net software as a background.34

The IFCS controller software consisted of the Stochastic Optimal Feedforward and Feedback Technique

(SOFFT) developed by Nesin Halyo and his colleagues at Information Control Inc. They produced this under

contract to NASA’s Langley Research Center, and the program was implemented by McDonnell Douglas’

Phantom Works in St. Louis.35 There, Urnes Sr., the chief of the SRFCS project for McDonnell Douglas,

assumed the same role for the IFCS as he had on the previous program. Prior control systems used only

feedback to do their jobs. The SOFFT algorithm, however, used a unique method to provide feed-forward as

well as feedback to the flight controls, and Stewart had worked with the control branch at NASA’s Langley

Research Center to enable the use of SOFFT in the development of the F-15 ACTIVE project. Langley itself

wanted to demonstrate the SOFFT on an aircraft that had a large number of interactive control affectors and

the F-15 ACTIVE, with its canards and thrust vectoring nozzles, was the ideal candidate

An upgraded neural network design was developed during Phase I, and then test flown in 1996. Using

the Levenberg-Marquardt feed-forward learning algorithm, this net performed within one percent of the

desired flight computations, doubling the accuracy over the networks flown in Phase 0. Meanwhile,

McDonnell Douglas had enlisted Tennessee State University to help explore the use of Adaptive Networkbased Fuzzy-Interference System, or ANFIS. 36 Fuzzy logic involves the employment of algorithms to arrive

at a decision instead of working from linear paths of calculation. The concept was attractive because of its

speed, and it better represented the ACTIVE’s non-linear stability and control derivatives. But it was too

large and resource-hungry to be used on a production airplane featuring IFCS capabilities.37 NASA

35 Annual report 3/95—7/96,1-1.

36 Annual Report, 3/94-7/96, p. 3-3.

37 Annual Report, 3/95-7/96, p.3-5.
35

evaluated a competitor algorithm, called Active

Selection, along with Levenberg-Marquart. Active

Selection functioned differently, picking the case

with the largest error, then learning within the

limits of that error. But in the end NASA chose

the Levenberg-Marquart for further development

since it demonstrated an overall error rate lower

than that of Active Selection.

The Phase II on-line learning neural networks

were developed using the NASA Ames Dynamic

Cell Structure (DCS) neural network format.

Pilots flew a combination of Phases I and III in

order to generate flight data for the Phase II online learning. A baseline pretrained neural net sent

signals to the flight controller, with the online net

calculating the differences between the outputs for

the actual system versus the predicted results. The

differences were added to the derivatives and also

sent to the flight controller. The test flights of

Phase I and III took place in March and April of

1999. The Phase II on-line learning software

required more development however, and was not

flight-tested. Nevertheless, inserting flight data into

the algorithm showed good promise for the design.

Validating Non-Deterministic Software

The problem that faced NASA engineers (and

will continue so long as they use neural networks)

involved verifying and validating nondeterministic software.38 Real-time embedded

John von Neumann, an early computer pioneer,

compared many of the functions of a digital machine

with those of the neural network in the brain. Research

showed a close relationship between computers and

the human brain, an important insight for aeronautical

researchers concerned with the interface in the cockpit

between machine and human. It also showed the

similarity between artificial neural nets (ANNs) and

computers.1 Others have made analogies between the

human brain and computers as well, including Norbert

Weiner, in his classic Cybernetic, or: Control and

Communication in the Animal and the Machine.

2

ANNs function much like the human brain. Most

learning in the mind occurs by natural neural

networks, made up of cells, called neurons, which act

on each other through electrical pulses. Since the early

days of artificial intelligence (AI), artificial neural

networks have held great promise because they could

Artificial Neural Networks

(ANNs): How They Work

1

 The writings of John von Neumann contain frequent references to

the similarities between the human brain and computers. See

Computer and the Brain (New Haven: Yale University Press, 1958).

2

 Norbert Weiner, Cybernetic, or: Control and Communication in the

Animal and the Machine (Boston: MIT Press, 1948).

See ANN, page 36

38 John Carter, interviewed by the author, Dryden Flight

Research Center, 9 April 2001.
36

model how a natural brain is organized and

how it works. Marvin Minsky, the well-known

AI specialist from the Massachusetts Institute

of Technology (MIT) began work on ANNs in

the late 1960s. He called the nodes

“perceptrons,” a term still occasionally used

today. Further work has been done on

applications of neural nets in many domains.

In situations where learning is required, they

are indispensable. At first, processors were too

slow and memories too small to adequately

support work on ANNs, but even in the case of

control applications that is no longer the case.

A neural net hosted by a computer usually

consists of several layers of nodes, typically

an input layer, a hidden layer or layers, and an

output layer. Invariably there are fewer output

nodes than input nodes. For instance, in a

flight control system, there might be an input

layer for each of two-dozen parameters in

each axis. These would be combined into a

smaller hidden layer, and probably

multiplied by a weight. The hidden layers

then come together in an output layer of one

node per axis that sends a signal to the

actuators.

Take an LED display in a bedside clock,

for example, with seven light segments which,

in combinations, are used to represent the

numbers 0 through 9. A “1” would have two

segments lit, while an ”8,” would have all

seven segments lit, and so on. If some

segments receive a relatively large signal

while others little or no signal, the ones with

large signals light up. Recognizing this, the

net tries to adjust the weights in the hidden

layer in order to light the desired segments by

balancing the signals. Preprogrammed to “see”

these circumstances and adjust to them, the

hidden layers remain invisible to the user who is

unaware of any imbalance in the clock.

ANNs are “taught” by rapidly running

thousands of cases. Pre-trained neural nets

“know” the appropriate weights, having seen

them in a simulator. Neural nets that learn on

the fly are more flexible, but are also more

difficult to implement, since they are limited

by computer power. The VMSC, like the

Hawk before it, could barely handle the

processing needs identified for what was to be

called the Intelligent Flight Control System

(IFCS).

ANN ... from page 35
37

software is difficult, if not impossible, for a computer to verify under normal circumstances.39 As a result, the

computer will arrive at deterministic answers during non-deterministic times. For instance, if the system is

supposed to calculate a seven, a correct program will result in a seven. But that seven may come at an

unexpected time, depending on the operating system, information from the environment, previous inputs, and

other demands on the computer. A neural net, by contrast, can seldom calculate a specific value. For example,

if a neural net inputs 26 values to calculate the command output in one axis, the output may not always be the

same – even if the inputs are all the same. The construction of the hidden layers may result in a different

value, although it may be in acceptable range. NASA and Boeing combined some test principles from the

real-time world with ones intended for neural nets, to form a fairly sophisticated test series.

The open loop nature of the flight controller in Phase I of the IFCS enabled it to be piggy-backed on the

regular flight test program of the F-15 ACTIVE. The system received sensor data as though it was connected,

but the output remained unconnected to the main flight controller. Instead, this data was compared to what the

generic fly-by-wire system commanded, which in turn was telemetered to the ground for later analysis; at this

stage it did not control the aircraft in real time. The ANNs ran on the VMSC and verification was,

accordingly, less formal. Phase III of the program used the outputs of Phase I’s software to control the

ACTIVE aircraft and this had to be verified and validated more stringently. During the IFCS testing, Boeing

purchased McDonnell Douglas and adapted the principle of “you fly what you test,” to the flight research

program, meaning that the flight version is used for all verification and validation activities.40 NASA itself

adopted this principle in verifying the flight software.

The pre-trained neural nets of Phase I were entirely resident on the VMSC. Written in the Ada language,

the ANN originally fit into channel C of the computer. For the combined Phase I- Phase III flight program, it

resided in Channels A and B, achieving redundancy. Only 512K of Electrically Programmable Read Only

Memory, and 256K of Random Access Memory, were needed out of two million words of storage.41 Output

signals to the flight computers could be checked across the channels. The controller resident in the flight

control computers was the SOFFT algorithm, which was largely hand-coded and also in the Ada language,

39 Chapter three in James E. Tomayko, Computers Take Flight (Washington, D.C.: NASA-SP-2000-4224, 2000) is devoted to

verification.

40 Testing Philosophy, p. 8.

41 Annual Report, 3/95/ - 7/96, p. 3-1.
38

though some parts were automatically generated using the Matrix-X system build environment. Matrix-X

was a software development tool that aided engineers producing the flight program.

Part of the feedback portion of the algorithm required the solution of a complex matrix equation called a

Riccati Equation. A non-linear equation, it is the foundation of optimum control such as that used in the

IFCS, and is used to continuously calculate the IFCS feedback gains that are critical to the safety and

performance of the aircraft. Normally, a Riccati solution determines the control system gains during the

design. Since damage necessitates continually new design “on the fly”, the Riccati Equation becomes critical

to the IFCS process. Stewart had himself developed a multi-rate digital Riccati Equation as part of his

doctoral dissertation, and the test aircraft carried the only Riccati Equation solver known to be in flight at that

time, a milestone in the opinion of the project managers.42 Hand-coded in the C language for ease of

expression, the operating system accommodated the Riccati solver as a background job. In the foreground ran

the SOFFT controller at 80 hertz. Time left after the execution of the controller was spent solving the Riccati

equation. In this way, and even though unscheduled, the Riccati outputs were updated every few cycles while

the controller gathered updates from the sensors at the rate of 80 hertz.

The developer of the Riccati solver used in the IFCS was a group at Washington University of St. Louis.

Dr. Massoud Amin led development of dynamic neural network software and, most important, on-line

computation of the Riccati Equation to be installed on the F-15 IFCS software. The Washington University

team faced a difficult design challenge in solving the Riccati Equation on-line, for it would be the first time

this had been accomplished in any aircraft control system. Solving this equation onboard, in real time, gave

the system the vital ability to adjust to any change in aircraft stability, such as that caused by failures or

damage to the controls. Their success paid large dividends.

Using the “what you test is what you fly” principle, the controller software would go through many

dissimilar software and hardware environments before being installed in the airplane. Building functionally

equivalent code with dissimilar software was an essential part of verification, for building software

twice – but differently each time – gives assurance that its designers understand it. In fact, functionally

identical but dissimilar software is the basis of the backup on an Airbus fly-by-wire commercial

transport.43

42 Stewart, interviewed by the author, 10 April 2001.

43 Tomayko, Computers Take Flight, p. 128.
39

The basic process of verification and validation of flight control laws, still used today, is iterative, and

proceeds through a number of steps.44 In this instance, linear and non-linear engineering analysis was applied

to the design. The flight software code was then generated by a combination of hand-coding and automatic

generation. The code was module-tested, and then the software underwent trials in a simulator with a pilot or

engineer in the loop. The codes were next placed under configuration control to curtail frequent changes to

the requirements and the integrity of the design. The sub-systems then were integrated, and the software

loaded on duplicates of the actual hardware in a high-fidelity simulator. By the time the software was finally

loaded on the real aircraft, whatever could be tested on the ground was checked.

This process differed from that used on the HIDEC F-15. The software tested at that stage was

functionally equivalent (but not line-for-line equivalent) to the flight load.45 For the IFCS, the Matrix-X tool

allowed automatic generation of Ada code based on the graphical representation of the design. The SOFFT

modules were relatively easier to verify because almost all of the software ran on each pass through the

control system, so the software was executed frequently. Nevertheless, all logic paths were tested in each

module. Test scripts were generated by Matrix-X exercise software “super blocks,” a major output of the

software tool.

Another, admittedly expensive, way to test the software, was to implement the functional requirements in

dissimilar ways and reconcile any discrepancies in output. The results of the hand-coded controller were

replicated in a dissimilar fashion by giving the SOFFT and Riccati solver algorithms to a knowledgeable

engineer and having the engineer replicate the functions.

The defects in the IFCS system were categorized in four groups: compiler/linker problems, auto coder

problems, hand-coded defects, and design and Matrix-X problems. Nearly all discrepancies were compiler/

linker problems, something expected in an environment new to the software engineers. The few auto-code

and hand-code errors originated with misuse of tools rather than misunderstood requirements. The piloted

flight simulation gathered inputs from the Boeing and NASA pilots into the dissimilar SOFFT implementations.

Tests run in June 1997 revealed only a couple of design errors, which were quickly fixed and re-tested. This phase

completed the testing conducted before the software was loaded onto the airplane. Since the same software was

used for each step of verification, the entire verification and validation process was relatively inexpensive.

44 Testing Philosophy, p. 8.

45 Ibid., 10.
40

Configuration control was an important part of the SOFFT development. At first, the file protections of

the UNIX operating system were used.46 But only one engineer had full privileges, and he would have to

make manual permissions changes. And so the project eventually adopted the Rational Apex Ada

development environment, with its configuration manager, source code development editor, and

debugger.

The software was then mounted on the airplane in order to run on the Motorola 68040 processor in the

flight control computer and the 88100 processor in the VMSC. Both processors had mature compilers and

linkers for both Ada and C languages, built by Tartan Laboratories in Pittsburgh, Pennsylvania. Both had

been used on other programs and had operated previously on the ACTIVE. Whatever could be tested on the

F-15 simulator, such as pilot handling quality evaluations, was then tested with the software installed.

The IFCS Test Flight Series

The first flight of the IFCS actually occurred on the 126th flight of the F-15 ACTIVE modified with the

highly advanced control system. As he had in the SRFCS program, Jim Smolka won the honor of flying the

initial mission on 19 March 1999.47 Gerard Schkolnik took the rear seat of the airplane on that flight. An

engineer, Schkolnik had been in charge of the software verification, and had invented the ADAPT (Adaptive

Aircraft Performance Technology) mode, a way of modifying the frequencies of the commands to filter out

undesirable characteristics.48 On this first flight, Smolka performed several maneuvers, including doublets in

all axes, aileron rolls, and tracking. The 1.2-hour flight ended by flying different Dial-a-Gain (DAG) settings,

which were adjustable by the pilot.

The afternoon of the same day Smolka flew again, this time with Capt. Dawn Dunlop in the rear seat.

They did an hour of accelerations and deceleration tests. Dunlop took a few minutes to get used to the F-15’s

handling, which felt more like that of an F-18 than a normal F-15.

These flights took place with the neural net now activated. But in this instance, it did not have to

determine what had failed—as had the System Impairment Detection and Classification module in the Self46 Ibid., 19.

47 James Smolka, interviewed by the author, Dryden Flight Research Center, 6 April 2001.

48 Gerard Schkolnik interviewed by the author, Dryden Flight Research Center, 9 April 2001.
41

NASA pilot Marty Trout and U.S. Air Force Captain Dawn Dunlop in front of the F-15B ACTIVE.

NASA Photo EC99 44997-7
42

Repairing Flight Control System—it simply compensated for any deviation from the model. Skipping the

identification step gave these tests the feeling of success right from the beginning.

Early in the flight test program, NASA wanted to check off the milestone achievements for IFCS, which

required test flying throughout the envelope. Normally, this would require numerous flights; in this case

however, the program managers decided to accomplish the envelope expansion in one flight by flying only

the perimeter of the envelope, involving Mach .5 and Mach 1.3. Smolka flew the test and the IFCS

performed magnificently. Tests included a high performance split-S (rapid roll to inverted flight, followed

by a 5-6g downward arc until reaching level flight). These maneuvers gave the Dryden engineers – and

especially Smolka – confidence that this system would prove worthwhile.

NASA research pilot Dana Purifoy, who flew chase on the first two flights, took the controls on number

3 the morning of March 23rd while Schkolnik sat in the back. They tested the ADAPT in other situations

and did several engage/disengage cycles, simulating transients. That afternoon, Dunlop and Purifoy flew

some more tracking, rolls, and Dial-a-Gain (DAG) settings. The F-15 carried on the rapid pace of the tests a

week later as Dunlop commanded, and Smolka crewed flight number 5. Again, doublets, rolls, tracking,

and other envelope expansions were on the flight card. On 1 April 1999, Purifoy and Schkolnik flew more

Dial-a-Gain settings, as well as some formation flying and tracking. Later in the day Dunlop flew with

Schkolnik in the back seat on yet more DAG maneuvers, such as rolls and more tracking. The next day,

Smolka flew while Schkolnik took the rear seat, conducting formation flying and tracking with various

DAG settings. They scrubbed a second flight that day before the ANN control system was engaged because

of a shutdown in one of the research channels.

It took nearly two weeks to fix this problem, and the ACTIVE returned to flying status on the 14 April

with two missions. Purifoy flew both, and Larry Walker – Boeing’s chief experimental test pilot – replaced

Schkolnik in the back seat on the second flight. The aircraft maneuvered at 1.2 Mach and 32,000 ft. on both

flights. NASA tried to make up for lost time on the 16th by inserting three flights on the schedule. For the

first flight Walker and Purifoy switched positions from two days earlier. Dunlop commanded the second

and third flights, with Schkolnik taking back seat; all three flights stretched the DAG envelope. The two

flights on 23 April concluded the test program, with Purifoy and Dunlop in the cockpit.

Only a little over a month elapsed between the first and fifteenth – and final – flight of the program. It is

a testament to the newly loaded software on the airplane that so many missions were flown in so short a

time. These flights of the pre-trained neural net steadily expanded the flight envelope from 0.5 to 1.3 Mach

and from 15,000 to 35,000 feet. It is significant that throughout this program the pilot reports sounded more

routine than had those of the SRFCS flights, for there seemed to be fewer problems to overcome.
43

Dr. James Stewart, project manager for the IFCS experiments, pilot Jim Smolka, and Ken Szalai, Director of

NASA DFRC.

NASA Photo EC93 42284-13
44

These flights completed the initial testing of an ANN system built on top of the SOFFT controller (a process

which Chuck Jorgensen and his team watched closely). Now, the next technological step will entail a neural net

that learns in real time. Such flights are planned on both the former ACTIVE aircraft and on a C-17 transport.
45

Epilogue: The Future of

Intelligent Flight Control

At the start of the twenty-first century, dynamic neural networks were on the verge of trials aboard actual

aircraft. Chuck Jorgensen and his associates at Ames waited in anticipation during the earlier, pre-trained

neural net flown by NASA, hoping for the chance to apply dynamic cell structure neural nets to the task of

learning to fly the F-15.49 Although these kinds of algorithms took much of the power of the VMSC, there

were plans to augment that computer with a processor called the Super Harvard Architecture Computer, or

SHARC. But these plans did not come to fruition before the 1999 flights were complete.50

While NASA and Boeing tested the learning ANN offline, events elsewhere came to bear on the

program. As far back as 1994, B.S. Kim, a doctoral student at Georgia Institute of Technology, and his advisor

Anthony J. Calise, suggested a new F-18 flight controller concept that linearized feedback, making control

computationally difficult, yet possible.51 An on-line neural net transformed the outer control loop from nonlinear to linear, rendering it controllable.52 Calise, a specialist in this field, eventually conducted research on a

helicopter controlled by neural nets. Joe Totah of NASA Ames knew of Kim and Calise’s proposal, and

ultimately adapted it so as to control an F-15 simulator.53 The total number of training pairs – 4,275 in Kim’s

thesis – represented a relatively small number for such applications 54.

49 Charles C. Jorgenson. “Feedback Linearized Aircraft Control Using Dynamic Cell Structures,” ISSCI Paper. Albuquerque: TSI

Press, 1998: 050.1 - 050.6

50 Boeing. “Intelligent Flight Control: Advanced Concept Program,” MDC 98P0026, Annual Report Period: 1 April 1997 to 31

March 1998, The Boeing Company.

51 Byoung S. Kim, and Anthony J. Calise. “Nonlinear Flight Control Using Neural Networks,” in Journal of Guidance, Control, and

Dynamics, Vol. 20, No. 1, January-February 1997.

52 Kim. “Nonlinear Flight Control Using Neural Networks,” Ph.D. Thesis, Georgia Institute of Technology, School of Aerospace

Engineering, December 1993, p. 26

53 Joseph J Totah. “Simulation Evaluation of a Neural-Based Flight Controller,” AAIA Conference Paper 96-3503-CP, 1996, pp. 259 – 266.

54 Kim. “Nonlinear Flight Control,” p.78.
46

56 Interview with James Urnes, Sr. of Boeing Phantom Works, by Christian Gelzer, 11 October, 2002.

Even though NASA planned to fly its F-15 with an ANN that learned dynamically, it recognized that the

tactical aircraft’s performance and usage differed considerably from a commercial airliner; it also understood

that this new technology would not be considered a success unless it was adaptable to this second

environment. Thus, the next step in planning was the application of the system to a C-17 transport. Aside

from having an operating envelope much closer to that of a commercial plane than the F-15, the C-17 came

with an organic digital flight control system, enabling a more effective adaptation to self-repair. Moreover, it

had 30 surfaces that could be controlled.55

But why should NASA contemplate two neural network projects? To begin with, dynamic learning has

yet to be proven. The ACTIVE F-15 provides an initial step in this direction, but its narrow purpose as an

aircraft yields data with limited applications. The C-17 is a much more suitable vehicle to test the adaptation

of this technology more broadly, and with commercial uses in mind.

Additionally, the technology remains anything but routine, though there has been a great increase in

other commercial neural net projects even while the NASA projects were proceeding. Indeed, the

technological dispersion of neural nets in control systems resembles the rapid expansion of digital fly-by-wire

technology, which itself began while NASA was still in the midst of the F-8 fly-by-wire program. But for the

foreseeable future, the simplest use of the IFCS will probably be as a backup on standard fly-by-wire aircraft.

In this way, the IFCS interfaces will not be overly complicated, and the ANN will not assume primary

responsibility for flight control, but serve instead, only as a redundant system.

Perhaps a sign of confidence in this project, Boeing began deploying complete digital fly-by-wire

controls in the newer model F-18 E/F. F-18s prior to this had a mechanical back-up system as a redundancy to

the digital FWB, reflecting the conservative approach to aircraft control. The newer EF model is controlled

entirely with fly-by-wire technology, with no mechanical back up. An active part of the aircraft is the

automatically reconfigurable intelligent flight control system, following the developments at NASA,

McDonnell Douglas/Boeing and the Air Force. In a recent incident, a test pilot flying the new model F-18/EF

in a low altitude-high speed pass at the Navy’s Patuxent River test site, experienced stabilator control failure.

The stabilator on the F-18 aircraft is the most critical control surface, providing virtually all the

maneuverability at high speeds. This F-18 was saved from a near-certain crash, however, by the back up,

reconfigurable intelligent flight control system, which rendered the aircraft controllable, and allowed the pilot

to land safely.56

55 Jerry Henry, interviewed, Dryden Flight Research Center, 10 April 2001.
47

Yet, the most advanced use of ANN technology will likely enjoy continued support from NASA. This

line of research has the potential to accelerate certain kinds of design, for an analog control computer

essentially must be rebuilt in order to command hardware different from the original design. By contrast, a

digital computer-based flight control system needs only to have the software changed in order to reconfigure

an aircraft. NASA has amply demonstrated this with the former F-15B ACTIVE, an aircraft with high

capacity digital computers and new software as the heart of its control system. Thus, although the software

may take a great deal of time to develop, and may be expensive, the advantages are evident: ease of

development and production, and aircraft versatility.

An ANN can “learn” how to fly new hardware dynamically, allowing a new flight control system to be

sorted out and flown without any prior design. This speeds aircraft production, since a portion of software can

learn how to fly a particular aircraft, and then all such software can be alike. Even if minute differences exist

within a group of aircraft, the differences would be neutralized, and the aircraft optimized, by the control

system. Chuck Jorgenson has already used this method to define the control system of a Mars flying aircraft.

He has also incorporated this technology into a sleeve embedded with sensors that “read” the human arm’s

grabbing and motion impulses, translating these into a control system. Such a system presents the possibility

of true hands-off flying. In 1995 his Ames colleague John Kaneshige used neural nets in the final version of

the Propulsion Controlled Aircraft software on a MD-11 successfully landed by Gordon Fullerton.57

The importance of the In-flight Flight Control demonstration of the F-15 self-learning ANN cannot be

over-stated. If flight-critical stability derivative coefficients can be calculated during flight, even with

unanticipated damage to the aircraft, it will alter the course of control system design. It will also reduce

design cost, and at the same time increase the ability of control systems to adapt to conditions that would

cause a crash of aircraft using today’s control system design.

Meanwhile, the average traveler may well encounter ANNs for the first time on automobiles. The

General Motors Corporation is experimenting with neural nets as the heart of a controller to provide default

values when sensors and actuators fail. This might enable motorists to get to a repair shop before the car stops

completely and strands them.

The use of neural networks is likely to blossom in today’s convergence of faster computer processors,

larger memories, and more efficient algorithms. As a consequence of NASA’s continuing concern with safety,

and its foray into systems of aircraft self-repair, neural networks promise to yield dividends for any industry

using a controller. The cars we own, the trains we ride to work, and the planes we fly, all stand to benefit from

57 John Kaneshige, interviewed, Ames Research Center, 12 January 2001.
48

this advanced technology. More importantly, the success of the real-time learning processes used in NASA’s

F-15B ACTIVE established a design paradigm for all engineering systems. Now the design process no longer

needs to analyze the effects of system failures, and can instead use the F-15 Intelligent Systems process to

continually maximize system performance under all operating conditions. This intelligent design process,

with its similarities to human learning and adaptation, will affect products ranging from washing machines to

nuclear reactors, from lawn mowers to electric power plants. What NASA, Boeing and other industries are

undertaking in the development of intelligent aircraft systems will have a profound impact on tomorrow’s

products and systems.
49

The MD-11 commercial airliner was configured to employ the IFCS for differential engine control. With Gordon

Fullerton as captain the jet is seen landing on Rogers Dry Lakebed using only engine thrust for flight control.

Not yet earmarked for customer delivery, the jet sports a plain metallic finish rather than airline livery. Tests of

the Propulsion Controlled Aircraft (PCA) stemmed from the crash of a United Airlines DC-10, which had lost

its flight control surfaces. The DC-10 crew managed to achieve moderate control by adjusting the engine thrust

but the jet cartwheeled and broke up on landing.

NASA Photo EC95 43247-2
50

Generation Next

The next generation IFCS program is being conducted, appropriately enough, on the

former ACTIVE test bed, an F-15B which flew the original tests. The aircraft is undergoing modifications and updates for the upcoming flight program.
51
52

63 funding – a nickname for a type of Air Force discretionary funding.

Ada – a computer language related to Pascal, and at one time selected by the Department of Defense as the official

language for all programs DoD used.

Ailerons – a movable hinged section in or near the trailing edge of an airplane wing for controlling the roll

movements of the airplane.

Angle of attack – the angle of the airplane’s lifting surfaces relative to the motion of the air.

Attitude – the position of an aircraft or spacecraft in relation to a given line or plane, like the horizon.

Bleeding edge – beyond the leading edge of technology.

Canard – the horizontal stabilizer of an aircraft when located forward of the wing or wings, illustrated by the

NASA F-15B number 837.

Control surfaces – the moving part of wings, horizontal stabilizers, and vertical stabilizers that control the direction

of an airplane.

Cooper-Harper scale – a scale from one to ten by which research pilots can indicate their subjective judgment of

the handling qualities of an airplane, one being the best. 1-3 means “meets desirable criteria (Level 1), 4-6 means

“meets acceptable criteria,” (Level 2), and 7-9 are “unacceptable” (Level 3). A rating of 10 represents loss of

control.

Deadheading pilot – a pilot commuting as a passenger to the next flight that he or she is scheduled to fly.

Doublets – a maneuver in which the nose of the aircraft is raised above the horizon and then lowered by equal

amounts.

Envelope – the altitude and speed limits of an airplane in a specific configuration.

Fuel venting – dumping fuel overboard, with or without intent.

Pilot-in-the-loop – a human being in the aircraft’s control path.

Gains – the ratio of control inputs by a pilot to the movement of the control surfaces.

Pitch capture – a method of rating handling qualities by having the pilot try to pull down or up into a target pitch

angle and match it without much of an overshoot.

Prop blast/wash – the wind caused by a rotating propeller.

Roll rate – the speed at which the airplane rotates around its longitudinal axis, typically measured in degrees per

second.

Sideslip angles – the attitude of an airplane in a skidding turn.

Stabilator – a horizontal surface that is all-moving and controls the pitch of an aircraft. The term derived from

combining horizontal “stabilzer” and “elevator”, neither of which describes an all-moving surface.

Glossary
53

Squawks – discrepancies and failed components noticed in flight.

Two-ship formation: two aircraft flying together in such a way that one can pilot can tell the other is “lead.”

Research flights are almost always conducted with a chase plane for outside visual feedback, creating a two-ship

formation.

Windup turns – maneuvers in a level turn that include a linear progression of one parameter, such as “0.5 g per

second” or “3 degrees per second.”

Wing warping – the twisting of the outer part of the wings’ trailing edges in equal-but-opposite directions for

controlling the roll.
54

Abbreviations

ACTIVE – Advanced Controls Technology for Integrated

Vehicles

ADAPT – Adaptive Aircraft Performance Technology

AFIT – Air Force Institute of Technology

AFTI – Advanced Fighter Technology Integration

ANFIS – Adaptive Network-based Fuzzy-Interference

System

ANNs – Artificial Neural Networks

ATF – Advanced Tactical Fighter

CAS – Control Augmentation System

DAG – Dial-a-Gain

EGE – Effectors Gain Estimation

EPROM – Electrically Programmable Read-Only

Memory

GE – General Electric

HIDEC – Highly Integrated Digital Electronic Control

HUD – Heads Up Display

IFCS – Intelligent Flight Control System

INS – Inertial Navigation System

JPL – Jet Propulsion Laboratory

LAMARS – Large Amplitude Multimode Aerospace

Research Simulator

LED – Light Emitting Diode

MTBF – Mean Time Between Failure

NASA – National Aeronautics and Space

Administration

RCM – Reconfigurable Control Mixer

SHARC - Super Harvard Architecture Computer

SIDC – System Impairment Detection and

Classification

SOFFT – Stochastic Optimal Feedforward and

Feedback Technique

SRFCS – Self-Repairing Flight Control System

STAR – Self-Testing and Repair computer

SYSDYN — SYStem DYNamics

TMR – Triple Modular Redundant circuits

VMSC – Vehicle Management System Computer
55

Bibliography

Most of the information about self-repairing and intelligent

flight control is available in NASA technical reports. The

second greatest amount of information was gained by

interviewing the participants. In this case I was lucky that two

men: Jim Stewart of NASA and Jim Urnes of Boeing served as

project managers in these endeavors. But a number of other

important contributors (listed below) shared their insights:

Interviews

John Carter, Dryden Flight Research Center, 9 April 2001.

Jerry Henry, Dryden Flight Research Center, 10 April 2001.

Chuck Jorgenson, Ames Research Center, 9 January 2001

John Kaneshige, Ames Research Center, 12 January 2001

Wilton Lock, Dryden Flight Research Center, 6 April 2001.

Aaron Ostroff, via telephone from Langley Research Center, 5

April 2001.

Robert Quaglieri, via telephone from Wright Aeronautical

Laboratory, Dayton, OH, 16 May 2001.

Gerard Schkolnik, Dryden Flight Research Center, 9 April

2001.

Jim Smolka, Dryden Flight Research Center, 6 April 2001.

Jim Stewart, Dryden Flight Research Center, 4, 6, 10 April

2001.

Joe Totah, Ames Research Center, 12 January 2001

Jim Urnes, via telephone from Boeing, St. Louis, MO, 20 April

2001.

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Boeing. “Intelligent Flight Control: Advanced Concept

Program,” STL 99P0040, Final Report, 15 May 1999, The

Boeing Company.

Boeing. “Intelligent Flight Control: Advanced Concept

Program,” MDC 98P0026, Annual Report Period: 1 April 1997

to 31 March 1998, The Boeing Company.

Boeing. “Intelligent Flight Control: Advanced Concept

Program,” MDC 97M0004, Annual Report Period: 31 July 1996

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Burske, J., and G. Sommer. “Dynamic Cell Structures,”

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Technical Memorandum 112198, May 1997.

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TSI Press, 1998: 050.1 - 050.6

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Martinetz, Thomas and Schulten, Klaus. “Topology

Representing Networks,” in Neural Networks, Vol. 7, No. 3,

1994.
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Miller, T., Sitton, R. and Werbos, P. Neural Networks for

Control. MIT Press, Cambridge, MA, 1990.

Neumann, John von. “Probabilistic Logics and the Synthesis

of Reliable Organisms from Unreliable Components, ” in

William Aspray and Arthur Burks, Papers of John von

Neumann on Computers and Computer Theory. Cambridge,

MA: Charles Babbage Institute Reprint Series for the History

of Computing, v. 12. The MIT Press, 1987.

Noor, Ahmed K. Computational Intelligence and Its Impact

on Future High-Performance Engineering Systems. NASA

Conference Proceedings Publication 3323. Hampton, VA:

June 27-28, 1995. Published January 1996.

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Mills Press, 1980.

Rattan, Kuldip S. “Evaluation of Control Mixer Concept for

Reconfiguration of Flight Control System,”

IEEE National Aerospace Electronics Conference

Proceedings, May 1985.

Reed, Russell D. and Robert J. Marks, II.

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Artificial Neural Networks. Cambridge: MIT Press, 1999.

Stifel, J.M., C. J. Dittmar, and M.F. Zampi. “Self-Repairing

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Period January 1980-October 1987, AFWAL-TR-88-3007,

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Stewart, James F. “Integrated Flight Propulsion Control

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Stewart, James F, and Thomas L. Shuck. “Flight-Testing of the

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Technical Memorandum, NASA-TM-101725, NASA Dryden

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Flight Controller,” AAIA Conference Paper 96-3503-CP, 1996.

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58
59

Appendices
60

Appendix A – SRFCS Flight Log
61

Appendix B – Flight Test Summary, SRFCS

F-15A No. 8 RTO: NASA Ames-Dryden
62
63
64
65
66
67
68
69
70
71
72

Appendix C – SRFCS Progress Report,

April 5, 1990
73
74
75

Appendix D – SRFCS Progress Report,

March 29, 1990
76
77

Appendix E – SRFCS Progress Report,

March 22, 1990
78
79

Appendix F – SRFCS Progress Report,

March 15, 1990
80

Appendix G – SRFCS Progress Report,

March 8, 1990
81
82
83

Appendix H – SRFCS Progress Report,

March 1, 1990
84
85

Appendix I – SRFCS Progress Report,

February 15, 1990
86
87

Appendix J – SRFCS Progress Report,

February 8, 1990
88
89

Appendix K – SRFCS Progress Report,

February 1, 1990
90
91

Appendix L – IFCS FRR, Structures (February 1999 Review)
92
93

Appendix M – IFCS FRR, (from February 1999 Review)
94
95
96
97
98
99
100
101
102

Appendix N – IFCS FRR, System Operation (February 1999 Review)
103
104
105
106

Appendix O – Report WL-TR-91-3025

Vol. I Self-Repairing Flight Control, Aug. 91
107
108
109

Appendix P – Pilot Reports

(Boeing Company 99P0040)

Intelligent Flight Control Final Report
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139

Appendix Q – Flight Test Matrix (Boeing Company 99P0040)

Intelligent Flight Control Final Report

Intelligent Flight Control System Flight Test Points
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141
142
143
144
145
146