標題: Robust Adaptive Control Scheme Using Hopfield Dynamic Neural Network for Nonlinear Nonaffme Systems
作者: Chen, Pin-Cheng
Lin, Ping-Zing
Wang, Chi-Hsu
Lee, Tsu-Tian
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: adaptive control;robust control;Hopfield-based dynamic neural network;Lyapunov stability theory
公開日期: 2010
摘要: In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L-2 tracking performance to stabilize the closed-loop system. The weights of Hop field-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.
URI: http://hdl.handle.net/11536/135566
ISBN: 978-3-642-13317-6
ISSN: 0302-9743
期刊: ADVANCES IN NEURAL NETWORKS - ISNN 2010, PT 2, PROCEEDINGS
Volume: 6064
起始頁: 497
結束頁: +
顯示於類別:會議論文