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Proceedings Paper

Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks
Author(s): Yunan Hu; Yuqiang Jin; Jing Li
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Paper 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.

Paper Details

Date Published: 2 September 2003
PDF: 4 pages
Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); doi: 10.1117/12.521986
Show Author Affiliations
Yunan Hu, Naval Aeronautical Engineering Academy (China)
Harbin Institute of Technology (China)
Yuqiang Jin, Naval Aeronautical Engineering Academy (China)
Jing Li, Naval Aeronautical Engineering Academy (China)

Published in SPIE Proceedings Vol. 5253:
Fifth International Symposium on Instrumentation and Control Technology
Guangjun Zhang; Huijie Zhao; Zhongyu Wang, Editor(s)

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