DOT/FAA/AM-02/7

Dennis B. Beringer

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

May 2002

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, VA 22161.

Applying PerformanceControlled Systems, Fuzzy

Logic, and Fly-By-Wire

Controls to General Aviation

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No.

DOT/FAA/AM-02/7

4. Title and Subtitle 5. Report Date

Applying Performance-Controlled Systems, Fuzzy Logic, and Fly-ByWire Controls to General AviationMay 2002

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

Beringer, D.B.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

FAA Civil Aerospace Medical Institute

P.O. Box 25082

Oklahoma City, OK 73125 11. Contract or Grant No.

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered

Office of Aerospace Medicine

Federal Aviation Administration

800 Independence Ave., S.W.

Washington, D.C. 20591 14. Sponsoring Agency Code

15. Supplemental Notes

This work was performed under Task AM-A-00-HRR-519.

16. Abstract:

A fuzzy-logic "performance control" system, providing envelope protection and direct command of

airspeed, vertical velocity, and turn rate, was evaluated in a reconfigurable general aviation simulator

(configured as a Piper Malibu) at the FAA Civil Aerospace Medical Institute. Performance of 24

individuals (6 each of high-time pilots, low-time pilots, student pilots, and nonpilots) was assessed during

a flight task requiring participants to track a 3-D course, from take-off to landing, represented by a

graphical pathway primary flight display. Baseline performance for each subject was also collected with a

conventional control system. All participants operated each system with minimal explanation of its

functioning and no training. Results indicated that the fuzzy-logic performance control reduced variable

error and overshoots, required less time for novices to learn (as evidenced by time to achieve stable

performance), required less effort to use (reduced control input activity), and was preferred by all groups.

17. Key Words 18. Distribution Statement

Document is available to the public through the

National Technical Information Service

Fly-by-Wire Control, Performance-Controlled

System, Flight Controls, Highway-in-the-Sky

Display, Pathway Display, Pilot Training Springfield, Virginia 22161

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price

Unclassified Unclassified 14

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

ACKNOWLEDGMENTS

The author extends his thanks to Barry Runnels, HFRL simulation engineer, for his assistance during

the preparation and conduct of the study and to Howard Harris for his assistance in scheduling simulator

sessions and assistance in data collection. Additional thanks to Dawn Loges at SymSystems for software

support. Special thanks to Noel Duerksen (Raytheon Aircraft, Wichita) for providing the control-logic

code and helping to make the system operational in the AGARS.

iii

1

 APPLYING PERFORMANCE-CONTROLLED SYSTEMS, FUZZY LOGIC, AND

FLY-BY-WIRE CONTROLS TO GENERAL AVIATION

BACKGROUND

In the opening of his book chapter titled Pilot

Control, Sheldon Baron stated, “The importance of

flight control to the development of aviation is difficult to overestimate” (Baron, 1988). Looking back

through the history of aviation, we can see numerous

efforts to make the human control of aircraft simpler,

less variable, and more reliable. The 1970s was a

particularly fertile period during which there was a

great interest in efforts to simplify the manual control

of systems, and one of those efforts was embodied in

the “performance control system” (PCS) for aircraft

(Bergman, 1976). This scheme allowed more direct

control of performance parameters than did “conventional” systems and had the potential for eliminating

undesirable aircraft behaviors and simplifying ab initio training. It is worth reiterating the history, as it

still applies to general aviation (GA) aircraft, although

some military and commercial air carrier aircraft employ what we could legitimately call performancecontrol logic.

The top-level goal for a flight is arrival at the

destination. This can then be decomposed to sub

goals, which involve the attainment of locations along

the chosen path that can be used to assess progress

toward the end goal. Progress toward these subgoals

can then be directed by causing the aircraft to move

toward those spatial subgoals, through manipulating

ground track, altitude, etc. However, manual control

of aircraft, using mechanical linkages in which control positions have a one-to-one correspondence with

positions of the aerodynamic control surfaces, does

not allow direct control of aircraft end-goal states.

Rather, the pilot must effect changes in attitude and

powerplant settings to cause changes in the higherlevel performance variables. Turning to a specific

heading, for example, requires the pilot to manipulate

roll rate (aileron position) directly, to achieve a desired turn rate (indirectly), which will ultimately

bring the aircraft to the desired heading. Mathematically, we have the pilot serving as at least a secondorder integrator and, in some cases, a third-order

integrator. (See Roscoe & Bergman, 1980; and Baron,

1988, for further discussion.)

A manual control task becomes easier to perform as

its “order” approaches zero (Roscoe and Bergman,

1980), that is, when the human operator directly

commands the end state of the system. We can achieve

closer to a zero-order system in two ways. The most

common means of accomplishing this in today’s aviation environment is the autopilot in GA aircraft, or

the Flight Management System (FMS) in corporate

and scheduled carriers. In the simplest case of flying a

heading, one sets the desired heading and the autopilot maneuvers the aircraft, at a specified limited rate of

turn, to attain that heading. A second way in which we

can achieve this result is to alter the control laws such

that the pilot uses control position to command

higher-level performance goals (for example, rate of

turn/bank angle; rate of climb/descent), attaining a

compromise between automated maneuvering and

the authority inherent in manually guided maneuvering. There are two benefits that accrue from the latter

approach. First, manual control is simplified relative

to achieving performance-goal states. Second, safety

is enhanced relative to conventional manual controls

in that return of the self-centering (spring-loaded)

side-stick to its centered position returns the aircraft

to straight-and-level flight.

One should keep in mind that the gains seen with

a PCS come at the expense of being unable to perform

such maneuvers as barrel rolls and loops (requiring

direct authority over control surfaces), which is not

usually a problem in everyday GA flying. Recall that

the PCS is commanding rate of climb and rate of

heading change (via bank angle) directly, and thus any

maneuver that would require a continuous non-zero

pitch-change rate or bank-change rate cannot be performed. Previous research results from the GA environment using a PCS (Roscoe & Kraus, 1973;

Bergman, 1976; Roscoe & Bergman, 1980) have

indicated significant reductions in both mean and

variable tracking error during the performance of

navigation tasks, as well as a reduction in workload.

These results were obtained both in a twin-engine

simulator and in a conventionally instrumented Twin

Bonanza with the PCS installed (controlled via a sidestick device), certified for normal flight operations

with few procedural restrictions.
2

Stewart (1994) also examined, in a GA simulator,

an implementation of a performance-control logic he

termed the “E-Z Fly” control system for GA aircraft.

Control was achieved through the normal control

yoke, but the operator commanded vertical speed and

rate of heading change. The throttle was used to

command airspeed directly. The control logic contained limits on the commandable range of flightperformance parameters so that dangerous or

unreasonable configurations could not be commanded

by the operator. The control system was used in

conjunction with a highway-in-the-sky-format (HITS)

primary flight display, and gain of the controls was

reduced on final approach to match the reduced width

of the HITS pathway as it narrowed down to the

runway width. Control forces were manipulated such

that they were reduced to zero when the controls were

moved to a new position and held there for more than

a few seconds.

The results reported by Stewart were from 3 pilots

and 7 non-pilots. Control of altitude, airspeed, and

lateral error was better for both groups when the E-Z

Fly system was engaged, and both groups exhibited

less accurate path tracking during turns than during

straight segments. Throttle-lever activity was reduced

using the E-Z Fly system, and all of the participants

preferred the E-Z Fly system over conventional controls.

Interest in applying simplified control schemes to

GA aircraft reappeared with the government/industry

Advanced General Aviation Transport Experiments

(AGATE) program. Program goals included simplifying the flight task, reducing ab initio training requirements, and increasing the safety of flight. In the

pursuit of these goals, an approach similar to the PCS

was investigated in which a “fuzzy-logic” controller

(FLC) was developed (Duerksen, 1996). Duerksen’s

goals were to create a “reusable” decoupled flight

controller that could be directly installed on different

airframes without the usual individual “tuning” associated with autopilot systems, and, with this fuzzylogic system serving as an expert-systems supervisor,

to provide control boundaries such as angle of attack

and airspeed limits. Duerksen’s efforts produced usable code that was then evaluated for its ability to

control an aircraft by using simulation. The code was

subsequently transported to the Advanced General

Aviation Research Simulator (AGARS) at the FAA’s

Civil Aerospace Medical Institute (CAMI) for pilot

performance evaluations.

METHOD

Subjects and Design

Twenty-four individuals (6 each of high-time pilots, low-time pilots, student pilots, and nonpilots)

participated in the study. Each participant served as

his/her own control, flying both the conventional

yoke and the side-arm FLC so that control configuration was a within-subject variable. Each flight consisted of 9 discernable segments that were used as a

second independent within-subject variable. Order of

presentation of control type was counterbalanced

across subjects. Dependent variables recorded included

lateral and vertical course-tracking error (via digital

recording), and control movements and blunder errors (via videotape).

Equipment

Data were collected in the AGARS configured as a

Piper Malibu with a highway-in-the-sky format navigation display, using a follow-me airplane symbol and

velocity vector on the copilot’s side of the panel. The

conventional system was flown with a back-loaded

yoke and separate power controls. The FLC system

was flown using 3 axes of a balanced, spring-centered

and damped 4-axis side-arm controller (Figure 1;

Beringer, 1999), with those axes representing turn

rate (wrist rotation), climb / descent rate (vertical

wrist flexion), and airspeed (fore-aft slide axis).

Procedures and Task

Following the signing of consent forms, participants were seated in the right seat of the AGARS for

a short pre-flight briefing. The functioning and movement of the controller they were to use for that flight

were described and demonstrated. Participants manipulated the side-arm control with their left hand,

necessitated by structural restrictions on control and

display placement, but were free to use either hand to

manipulate the yoke. They were also shown the navigation display and given an explanation of its symbology and functioning. The simulator’s engine was then

started, and the flight was begun without any actual

hands-on training in the use of the controls and

displays. The experimenter, seated in the left seat,

monitored the participant’s progress and intervened

only when it was necessary to limit extreme excursions

of the simulator, to manipulate power in the conventional-controls configuration, or to prevent stalls or

ground contacts. The pattern required a continuous

climb from lift-off until the base-leg turn and then a

descent on the approach.
3

The task required the participant to take off, establish a climb to intercept the pathway depicting the

desired 3-D courseline, and follow the command

guidance indicator (follow-me airplane) by aligning

the aircraft velocity vector with the follow-me airplane. The subject was required to follow the command indications through a greatly extended pattern

with a lengthened down-wind leg that turned back

toward the airport (Albuquerque, runway 08) at the

Albuquerque VOR and followed the instrument landing system (ILS) approach back to the runway. The

flights ranged from 15 to 17 minutes. After a short

break, the participant and experimenter discussed the

functioning of the second controller to be used and

performed a second flight with that controller. On

both flights, the simulator entered actual instrument

conditions on initial climbout and no external visual

cues were available to the subject until breaking out

just before landing. The session was concluded with

post-test questionnaires about previous flight experience and about the participant’s ratings of the 2

control systems.

RESULTS & DISCUSSION

Tracking Error

Analyses indicated that there were substantial reductions, as seen in the Bergman studies, in both

mean and variable errors in the vertical and horizontal

dimensions when the FLC was used, as compared

with the conventional controller (yoke). Overall analyses by group and control type indicated that use of the

FLC produced less error, both horizontally and vertically (Figure 2). There were significant reductions in

root-mean-square error (RMSE) for both vertical

[F(1,40)= 18.11, p<0.0005] and lateral

[F(1,40)=14.06, p<0.001] and a shift in lateral bias

(mean) error [F(1,40)=21.09, p<0.00001)] (overall

bias was to the left of courseline).

Much of the error reduction came in the turns, and

Figures 3 and 4 show raw data plots for one nonpilot

over the complete course. One can see in Figure 3 that

the performance with conventional controls was far

more variable, with the aircraft repeatedly flying

through its commanded altitude. Figure 4 shows the

lateral-error plot, and it also contains greater departures from desired track (and more variability) for the

conventional control than for the FLC. One can also

see where turns 3 and 4 were overshot considerably.

These performance differences between control systems were consistent across all 4 groups of subjects,

although the high-time pilots and 2 of the student

pilots tended to fly more precisely, regardless of the

type of control system used. Analyses also revealed

some intraserial transfer (as found by Bergman), in

that the conventional system initially fared worse if

preceded by the FLC than when the conventional

system was flown first. This effect dissipated over

flight segments, however. No such effect was apparent

for the FLC system. Overall, time to achieve stable

Figure 1. Four-axis side-arm controller used to implement the fuzzy-logic control

inputs (Beringer, 1999).
4

Figure 2. Main effects of control type for 3 measures of error.

0

20

40

60

80

100

120

140

160

180

Yoke Fuzzy Logic

Controller type

Vertical RMSE

Lateral RMSE

Lateral mean

Figure 3. Plot of raw vertical error for 1 nonpilot.

-400

-300

-200

-100

0

100

200

300

400

1 121 241 361 481 601 721 841

samples (sec)

Fuzzy Logic

Yoke
5

performance was less for the FLC than for the conventional system. (The large spike at the beginning of the

record is an anomaly in path error calculation that

occurred prior to path capture.)

There were also the expected significant differences

between the pilot groups (Figure 5), with the highand low-time pilots exhibiting somewhat less error

(lateral and vertical RMSE) than the nonpilots and

student pilots [F(3,40) for each: vertical mean, F=3.22,

p<0.05; vertical RMSE, F=5.23, p<0.005; lateral

RMSE approached significance, F=2.8, p=0.0518].

Group means are presented in Figure 5. No interactions between control type and pilot group were

significant.

Control-Input Frequency

One could predict from an analysis of required

control motions that the FLC should produce at least

a 2:1 reduction in the frequency of observed control

movements. That is, to enter and hold a given bank

angle, the yoke requires at least 2 deflections (one to

initiate and 1 to neutralize aileron at the desired bank

angle), whereas the FLC requires a single deflection to

the position corresponding to the bank angle (turn

rate). Table 1 depicts data for 4 individuals, 1 randomly selected from each group (non-pilot, student

pilot, low-time pilot, high-time pilot), sampled for 30

seconds from Turn #1 (T1: crosswind leg), Turn #3

(T3: base leg), and final approach (App). With 2

exceptions in which the ratios are higher, the comparisons evidenced an approximately 3:1 reduction in

control movements (defined as directional reversals)

from the conventional control (Y) to the FLC (F).

This measure can be thought of as an index of controlling workload, and the selected data shown are representative of the larger sample.

Participant Ratings

Participants rated each of the control systems for

the degree of effort required during takeoff, climb,

turns, level flight, descent/approach, and landing.

The rating scale used 9 points from 1 (minimum

effort) to 9 (maximum effort). Overall, the FLC was

preferred over the conventional system (Figure 6),

and participants rated the former as easier to use

[F(1,40)=34.73, p<0.00001]. Those who were not

pilots indicated that the FLC was easier to learn to use

(Figure 6). Although there were some minor meanrating differences between groups, pilot group was

not a significant factor, nor was there a significant

interaction between control type and pilot group.

Figure 4. Plot of raw lateral error for 1 nonpilot.

-500

-250

0

250

500

750

1000

1250

1500

1 121 241 361 481 601 721 841

samples (secs)

Fuzzy Logic

Yoke

Overshoot

Overshoot
6

Table 1. Frequency of control inputs by maneuver for 4 individuals, 1

from each pilot group.

Turn1 Turn 3 Approach Means

Pilot

Type

Control

Axis

Yoke FLC Yoke FLC Yoke FLC Yoke FLC

Non Pitch na na 10 0 4 5 7.0 2.5

Roll 12 11 18 5 29 3 19.6 6.3

Stu Roll 28 9 na na na na 28.0 9.0

Low Pitch 10 1 4 0 4 2 6.0 1.0

Roll 24 12 20 7 24 5 22.6 8.0

High Pitch 8 0 8 1 6 2 7.3 1

Roll 11 4 16 6 10 2 12 4

Figure 5. Three error measures by group.

0

20

40

60

80

100

120

140

160

180

nonpilot student pilot low-time pilot high-time pilot

Group

Vertical mean

Vertical RMSE

Lateral RMSE
7

When examined by flight segment, the biggest

difference in ratings was found in the turns for all but

low-time pilots. The one complaint with the FLC was

the same as that in the Bergman studies. That is, users

did not like having to hold the side-stick control

deflected for long periods of time to maintain a climb

or descent, although they rated it as “acceptable,” due

to a low spring force in that axis. This has since been

addressed, and pilots can now “lock-in” desired performance and control position, thus alleviating the

need to continually hold the control in the deflected

position, coupled with a release mechanism triggered

by force applied to the control.

Two additional observations are worth making

regarding the results. First, there should not have been

a significant effect of hand used for manipulating the

FLC, in this case due to the low spring loading and

comparatively low required input frequency. If there

was an effect for the predominantly right-handed

sample, then we are seeing the worst-possible case for

this implementation. Second, the effect does not

appear to be restricted to a specific display type, as

earlier positive results (Bergman) were obtained with

conventional instrumentation.

CONCLUSIONS

The findings were consistent with previous PCS

studies, indicating that the FLC system can provide

stable and less variable course and altitude tracking

performance than a conventionally configured system

when used as a manual control. This makes the system

a potential alternative for next-generation GA aircraft

from pilot-performance and pilot-training standpoints.

However, there are 2 considerable concerns that must

be addressed prior to application in a production

aircraft. First, the cost of the system must come down

to the point where it is an economic competitor with

other means of aircraft control (meaning affordable

and certifiable control computer, servos, etc.). Second, the debate must be resolved over reliability and

reversion modes and their effect upon training. If the

system is to be implemented in a class of aircraft where

no other means of control will be available as a backup (only 1 type of control is trained), reliability must

be sufficiently high. If, however, a reversion mode is

provided to allow for a slightly less reliable FLC, then

one either adds the cost of redundant identical systems, or one employs a stand-by mechanical linkage

system and incurs the cost and complexity of training

the pilot to operate both types of control systems.

Again, it ultimately comes down to where one wishes

to incur the cost for potentially increased safety and

ease of operation.

Figure 6. Mean perceived-effort ratings by group and control type.

1

2

3

4

5

6

7

8

9

Nonpilot Student Pilot Low-time Pilot High-time pilot

Participant Type (Group)

yoke

fuzzy logic

9 = maximum effort

1 = minimum effort
8

REFERENCES

Baron, S. (1988). Pilot Control. Chapter 11 in E.L.

Wiener and D.C. Nagel (eds.), Human Factors

in Aviation. San Diego, CA: Academic Press,

327-85.

Bergman, C.A. (1976). An airplane performance control system: A flight experiment. Human Factors,

18:173-81.

Beringer, D.B. (1999). Innovative Trends in General

Aviation: Promises and Problems. Chapter in

David O’Hare (ed.), Human Performance in General Aviation. Brookfield, Vermont: Ashgate Publishers, 225-61.

Duerksen, N. (1996). Fuzzy logic adaptive decoupled

flight controls for General Aviation airplanes.

Doctoral dissertation. Wichita, KS: Wichita State

University.

Roscoe, S.N. & Bergman, C.A. (1980). Flight performance control. Chapter 4 in S.N. Roscoe (ed.),

Aviation Psychology. Ames, Iowa: Iowa State University Press, 39-47.

Roscoe, S.N. & Kraus, E.F. (1973). Pilotage error and

residual attention: The evaluation of a performance control system in airborne area navigation.

Navigation 20: 267-79.

Stewart, E.C. (1994). A piloted simulation study of

advanced controls and displays for general aviation airplanes. In Proceedings of the 19th Congress of

the International council of Aeronautical Sciences,

AIAA document ICAS-94-8.1.3, 272-81.