PSEUDO-CONTROL HEDGING : A NEW METHOD FOR ADAPTIVE CONTROL

In the application of adaptive flight control, significant issues arise due to vehicle input characteristics such as actuator position limits, actuator position rate limits, and linear input dynamics. The concept of modifying a reference model to prevent an adaptation law from “seeing” and adapting-to these system characteristics is introduced. A specific adaptive control method based on this concept, termed Pseudo-Control Hedging, is introduced that accomplishes this for any Model Reference Adaptive Controller that includes approximate feedback linearization. This method enables continued adaptation while the plant input is saturated. Acceptance and flight certification of an online Neural Network adaptive control law for the X-33 Reusable Launch Vehicle technology demonstrator is discussed as motivation for this work. Simulation results applying the method to the X-33 are described.

[1]  Richard Monopoli,et al.  Adaptive control for systems with hard saturation , 1975, 1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes.

[2]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[3]  Jing Sun,et al.  Modified model reference adaptive control with saturated inputs , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

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

[5]  Michael Bryan McFarland.  Adaptive nonlinear control of missiles using neural networks , 1997 .

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

[7]  Seungjae Lee,et al.  Direct adaptive reconfigurable control of a tailless fighter aircraft , 1998 .

[8]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[9]  Charles Hall,et al.  X-33 Attitude Control System Design for Ascent, Transition, and Entry Flight Regimes , 1998 .

[10]  Anthony J. Calise,et al.  Adaptive nonlinear flight control , 1998 .

[11]  Tian-Yue Jiang.  Combined model- and rule-based controller synthesis with application to helicopter flight control , 1998 .

[12]  John M. Hanson,et al.  ASCENT, TRANSITION, ENTRY, AND ABORT GUIDANCE ALGORITHM DESIGN FOR THE X-33 VEHICLE , 1998 .

[13]  G. Rovithakis.  Nonlinear adaptive control in the presence of unmodelled dynamics using neural networks , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[14]  Kevin A. Wise,et al.  Flight testing of a reconfigurable flight control law on the X-36 tailless fighter aircraft , 2000 .