A Flight Test Demonstration of On-line Neural Network Applications in Advanced Aircraft Flight Control System

Fola Soares

Contek Research, Inc., El Segundo, California, U.S.A fola@contekresearch.com

1 Introduction The objective of NASA’s Intelligent Flight Control System (IFCS) program is to develop and flight test control schemes that enhance control during a pri-mary control surface failure or aerodynamic change due to a failure or modeling errors. The goal of the Generation 2 (Gen-2)] is to evaluate on-line neural flight control system that can provide adaptive control without explicit parameter identification. The Gen-2 approach does not require (1) informa-tion on the nature or the extent of the failure, (2) knowledge of the control surface positions, or (3) information on aerodynamic failures or un-modeled parameters. This tracking controller adds direct adaptive neural network signals to the control law1,2,3,4. These neural networks are used to gener-ate command augmentation signals to compensate for errors due to un-modeled dynamics, including dynamics due to damage or failure. Flight demon-stration started early in the year 2006, on the NASA F-15 tail number 837. The F-15 6-degree-of-freedom (6-dof) simulator was used in the evalua-tion test, comparing stabilator and canard failure compensation with the neural network algorithm. For this specific project objective, neural network based adaptive controllers can only be used safely if proper Verification and Validation can be done. Due to the nonlinear and dynamic nature of an adaptive control system, traditional Verification and Validation (V&V) and certification techniques are not sufficient for adaptive controllers, which is a big barrier in their deployment in the safety-critical aerospace applications.

2 Research Overview and Background The use of adaptive control system has been grow-ing in flight control. In the past few years, there has

been an increasing interest within the control com-munity in exploring the promise of biologically motivated algorithms, like fuzzy sets, neural net-works as well as genetic algorithms to solve diffi-cult optimization and control problems5,6. Recently, an on-line adaptive architecture that employs a Sigma-Pi neural network has been applied to aug-ment the attitude control system7 and it has been shown that the on-line neural network in adaptive control architecture is very effective in dealing with the performance degradation problem of the trajec-tory tracking control. Melin and Castillo8 have also used adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Extensive research has also been done to investigate the certification of an adaptive flight control9. Highly reliable adaptive control systems are needed to fulfill the present and future aerospace needs. The verification and valida-tion (V&V) of adaptive neural flight control sys-tems10 is of great interest.

2.1 Description of the Research Vehicle A highly modified pre-production F-15B aircraft was used for the IFCS project. The most visible modification is the inclusion of a set of canards near the pilot station (Figure 1). The canards are a set of modified horizontal stabilators from a Boeing F-18 aircraft. The thrust vectoring feature is not used in the Gen-2 controller, and the canards are not used for direct longitudinal control in the Gen-2 control system. The airplane is controlled by a quadruplex redundant, digital, fly-by-wire flight control system. The canards will be used for the [A] matrix aerodynamic failure experiment.

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Copyright © 2007 by Fola Soares. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

3. Description of the Gen-2 Controller The control scheme is based on an adaptive neural controller canceling the errors associated with the dynamic inversion of the model. Initially, constant values of aerodynamic stability and control deriva-tives for a fixed condition in the flight envelope are used for model inversion. The reference models are based on pilot preferences18. See Figure 2 for the Gen2 control structure using a direct adaptive control method. The yaw axis controller is not a reference based controller, but rather a classical yaw system. An adaptation signal is added to the classical yaw command (see Figure 3). The simpli-fied dynamic inversion algorithm inverts a B matrix with states for modeling the short period and roll modes. To protect against a poorly ranked [B] matrix, inversion is by a pseudo-inverse. The inver-sion is used to determine the necessary control surface deflections. B is the state space system control matrix. 3.1 Classical Yaw Axis Beta-dot Controller The initial research controller was a simplified dynamic inversion type controller for all three con-trol axes; roll, pitch and yaw. For the original dy-namic inverse controller, the proportional, integral, and derivative (PID) gains were tuned to achieve linear stability robustness and aero-servo-elastic (ASE) mode attenuation for the nominal (no fail-ure) case. Subsequent re-designs of the simplified controller with or without neural networks were not

able to modify this behavior and pilot comments from simulation sessions continued to be negative.

During the design, with or without neural networks, with locked stabilator failure simulations, signifi-cant lateral acceleration (ny) and angle of sideslip (�) excursions were observed. To reduce the lateral accelerations and sideslip excursions, the research controller was modified. This was accomplished by using a �-dot term as the primary feedback to the classical controller for the yaw axis, while continu-ing to use the original dynamic inverse control for the pitch and roll axes. The effect is to decouple lateral and directional axis in the research control-ler. This modification was necessary to obtain rea-sonable flying qualities in the presence of a simu-lated failure. 3.2 Conventional (non-adaptive) Feedback Control System Figure 1 illustrates the basic anatomy of a simple conventional control system. In the case of an aviation example, sensors measure the aircraft state parameters to be controlled (e.g., pitch angle). After some signal conditioning, the measured state is compared to the desired state to generate a meas-ure of the difference or error. The controller’s function is to produce control inputs to be sent to the aircraft control surfaces to reduce the measured error.

Figure 1: Conventional (non-adaptive) feedback control system.

The most widely used traditional non-adaptive design is the PID (Proportional Integral-Differential) controller, due to its simplicity, per-formance and robustness. A PID controller forms a

control signal that is proportional to either the error itself, the integral of the error signal, and/or the derivative of the error signal19:

( )( ) ( )��

���

�++= � ))( errordtd

KdterrorKerrorKControl DIP (1)

Desired State

Sensors Controller

Signal Conditioning

Error

Measured State

f(error, gains)

The proportionality constants (KP, KI and KD) are called the gains of the system and are normally vectors for multi-input, multi-output systems (MIMO). Tuning the controller is a matter of find-ing the right gain settings. If the gains are selected too large, the system may exhibit instability; yet if selected too low, the system response may become sluggish. Although fairly simple to implement, PID and other types of conventional controllers unfortunately have the limitation that once the controller is put into operation, the gains cannot be changed. If the performance of the controller degrades after start-up, the only remedy is to stop the controller, re-tune the gains, and then restart the controller. This process continues until a combination of parameters is found that produces the desired results20. If the aircraft or spacecraft to be controlled or its operat-ing environment should change significantly, new

gains will be needed to optimize performance. The challenge is that re-tuning the gains may not always be practical if the behavior of the process changes too frequently, too rapidly, or too much. The tun-ing process can be excessively time consuming. Moreover, changes to the plant that favor increased gain settings may also cause the control system to become unstable. 3.3 Adaptive Control System Architecture Figure 2 provides a notional diagram of an adaptive control system to illustrate the role of learning. The controller gains are not fixed, but rather learned by neural network. The control law is dynamic inver-sion controller (DINV) with a model-following command path, which calculates the control gains based on adaptive system identification.

Figure 2. Generic adaptive control system

4 Research Challenges 4.1 Software Verification and Validation A serious challenge hindering the deployment of advanced, flight-critical software is the requirement to show that it can operate as intended and with very high reliability. Rigorous methods for adap-tive software verification and validation must be developed to ensure that disabling control system software failures will not occur, to ensure the con-trol system functions as required, to eliminate unin-tended functionality, and to demonstrate certifica-tion requirements can be satisfied. For this purpose, NASA is conducting IFCS flight tests research aimed at developing usable procedures and meth-ods that can verify the reliability of adaptive flight control system software11,12,13,14,15. Adaptive con-

trollers that can make rapid and automatic adjust-ments to enable self-healing in the event of vehicle damage, might also act to make a healthy aircraft un-flyable or a safety hazard to other vehicles. The software implementation must be thoroughly ana-lyzed and checked to provide sufficient assurance of its intended functionality, safety, and the absence of aberrant functionality.

4.2 Special V&V Challenges of Adaptive Control Software The verification complexity posed by adaptive control systems primarily stems from the use of the learning algorithm. It is fairly easy to realize (from Figure 2) that if the controller gains are based on numerical values passed from the learning algo-rithm to the controller, then malfunctioning of the learning algorithm can lead to the calcu

Pilot or Autopilot

Adaptive Systems

System Identification

�Er-

ror

Feed- Back Conv

Aircraft or Spacecraft

Controller f (SI, Error)

“Input”

“Output”

�

Control Augmentation

Vehicle State

lation of controller gains that are too low or too high, hence produce sub-optimal controller per-formance. The special challenges of adaptive con-trol systems relate to maintaining stable and con-vergent learning. Adaptive control systems derive their adaptive utility by using learning algorithms to identify transfer matrix models or determine the coefficients of neural networks. In many cases, learning algo-rithms update parameters as follows:

( )errorLearningKTT Learnii *1 += −

(2)

( )edictedMeasuredLearnii yyKTT Pr1 * −+= −

(3)

Where Ti is the parameter values (e.g., transfer matrix or neural network weights) at step i and the “learning error” is typically computed as the differ-

ence between the measured plant state (y) and the predicted plant state. The predicted plant state is usually a function of the identified parameters, Ti. KLearn is the adaptation learning gain and its selec-tion plays a large role in algorithm stability and learning rate. A difficulty is finding suitable values for the learning gains that provide stable adapta-tion, while allowing convergence to the desired solution in a sufficiently short time. 5 Experimentation The Generation II concept is a dynamic inversion controller with a model-following command. The feedback errors are regulated with a proportional plus integral (PI) controller. This basic control system is augmented with an Adaptive Neural Net-work that operates directly on the feedback errors. The Adaptive Neural Network adjusts the system for un-predicted behavior, or changes in behavior resulting from damage.

Figure 3: NF-15B NASA -837 Research Aircraft

5.1 Test Conditions and Maneuver Sequence Four test conditions are indicated within the flight envelope, but only Flight Condition 1 (FC1) the primary flight condition during this IFCS flight test phase is the subject of this paper. Two types of simulated control system failures are available: locking one stabilator at a selected angle from trim, and changing the programmed canard response gain factor (canard gain multipliers). IFCS configura-tions: conventional mode, default enhanced mode, Neural Net ON, default enhanced mode with simu-lated failures introduced, and Neural Net ON with simulated failures introduced. FC1: Mach 0.75, 20000-ft altitude with simulated stab failures and adaptation with and without adaptive neural net-works.

6 Results, Discussion and Conclusions Two types of simulated failures were tested during the flight. First, an aerodynamic type of failure, inserts a multiplier onto the canard surface com-mand (change in Cmalpha). Second, a surface fail-ure, inserts a jammed stabilator failure [B matrix]. Results from the simulation are presented to illus-trate the flight test flown under FC1 that highlight the benefits provided by the Gen 2 control system. The controller is a rate command system, therefore the attitudes such as bank angle (phi) are for com-parison purposes only; and as such are used for disturbance rejection trade-off studies. At FC1 (0.75 20K) , the flight tests achieved most of “around-the-envelope” test. The simulated failure case setting Canard multiplier to -0.5, neural net-work removes oscillation. The pitch rate follows pilot pitch command closer than without NN. The simulation predicts a sensitivity to pitch rate distur-bances, which roughly correlates to the oscillations observed in flight. Simulation should predict any potential serious consequences. When the dynamic inversion controller gains reduced to meet ASE attenuation requirements it was much harder to achieve desired performance but NN contribution increased. Explicit cross terms in NN required for failure cases Dynamic Inversion controller contributes signifi-cantly to cross-coupled response in presence of

surface failure (locked) with the redesigned yaw loop using classical techniques. NN’s require care-ful selection of inputs. To achieve robust full enve-lope performance significant amount of “tuning” was required. This appears to contradict the claim of robustness to unforeseen failures. For simulated failures, benefits were obtained using adaptive on-line neural networks compared to the non-adaptive controller as predicted by the V&V tools developed under the IFCS project1,20. Figure 4 is the CAP calculated from LOES fit of pitch frequency sweep performed in flight, with NN on shown in pink and NN off in blue. Overall, IFCS flight-tests currently collecting “real world” flight experience for adaptive controls. F-15 IFCS project is providing valuable research to promote adaptive control technology to a higher readiness level This paper demonstrates the application of online adaptive control system as demonstrated by actual flight tests flown the IFCS project at NASA DFRC, California. The flight test data were compared with the Matlab/Simulink based V&V tools developed under the IFCS Project. The V&V tools predictions are similar to those obtained from the flight test. This shows that our V&V tools have future applica-tion in the V&V of adaptive NN based controllers.

Figure 4: CAP References

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