Reconfigurable flight control system design using adaptive neural networks

An adaptive controller design method based on neural network is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness deficiencies caused by control surface damage. The neural network based adaptive nonlinear controller is developed by using the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line learning neural networks are implemented to compensate the control effectiveness decrease and guarantee the robustness to the uncertainties due to aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed by assuming that all of the nonlinear functions of the system have uncertainties, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks learn through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated. A nonlinear dynamic model of a high performance aircraft is used to demonstrate the effectiveness of the proposed control law.

[1]  Yuri B. Shtessel,et al.  Multiple Timescale Flight Control Using Reconfigurable Sliding Modes , 1999 .

[2]  E. Morelli.  Global nonlinear parametric modelling with application to F-16 aerodynamics , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[3]  Robert F. Stengel,et al.  Flight Control Design using Nonlinear Inverse Dynamics , 1986, 1986 American Control Conference.

[4]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[5]  Anthony J. Calise,et al.  Nonlinear flight control using neural networks , 1994 .

[6]  Anthony J. Calise,et al.  Development of a Reconfigurable Flight Control Law for Tailless Aircraft , 2001 .

[7]  Chang-Chieh Hang,et al.  A direct approach to adaptive controller design and its application to inverted pendulum tracking , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[8]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[9]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[10]  J. Karl Hedrick,et al.  Nonlinear flight control design via sliding methods , 1990 .

[11]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[12]  William L. Garrard,et al.  Nonlinear inversion flight control for a supermaneuverable aircraft , 1992 .

[13]  W. D. Morse,et al.  Model following reconfigurable flight control system for the AFTI/F-16 , 1990 .

[14]  Anthony J. Calise,et al.  Adaptive nonlinear control of agile antiair missiles using neural networks , 2000, IEEE Trans. Control. Syst. Technol..

[15]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[16]  Anthony J. Calise,et al.  Nonlinear adaptive flight control using neural networks , 1998 .

[17]  Anthony J. Calise,et al.  Adaptive Model Inversion Flight Control For Tiltrotor Aircraft , 1998 .

[18]  Pramod P. Khargonekar,et al.  Stability analysis of a missile control system with a dynamic inversion controller , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[19]  William L. Garrard,et al.  Nonlinear control of a supermaneuverable aircraft , 1989 .

[20]  Youdan Kim,et al.  Nonlinear Adaptive Flight Control Using Backstepping and Neural Networks Controller , 2001 .

[21]  Y. Ochi,et al.  Design of restructurable flight control systems using feedback linearization , 1991 .

[22]  Anthony J. Calise,et al.  Analysis of Adaptive Neural Networks for Helicopter Flight Control , 1997 .

[23]  R. Walker,et al.  Nonlinear flight test trajectory controllers for aircraft , 1987 .

[24]  Dale F. Enns,et al.  Nonlinear control law with application to high angle-of-attack flight , 1992 .

[25]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.