Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks

Yunan Hu; Yuqiang Jin; Jing Li

doi:10.1117/12.521986

2 September 2003 Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks

Yunan Hu, Yuqiang Jin, Jing Li

Proceedings Volume 5253, Fifth International Symposium on Instrumentation and Control Technology; (2003) https://doi.org/10.1117/12.521986
Event: Fifth International Symposium on Instrumentation and Control Technology, 2003, Beijing, China

Abstract

Based on fully tuned RBF neural networks and backstepping control techniques, a novel nonlinear adaptive control scheme is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The nonlinear adaptive controller is designed using backstepping control techniques. The control problem is resolved while the control coefficient matrix is unknown. The adaptive tuning rules for updating all of the parameters of the fully tuned RBF neural networks are firstly derived by the Lyapunov stability theorem. Finally, nonlinear 6-DOF numerical simulation results for a BTT missile model are presented to demonstrate the effectiveness of the proposed method.

Citation Download Citation

Yunan Hu, Yuqiang Jin, and Jing Li "Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks", Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); https://doi.org/10.1117/12.521986

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