標題: Indirect Adaptive Control Using Hopfield-Based Dynamic Neural Network for SISO Nonlinear Systems
作者: Chen, Ping-Cheng
Wang, Chi-Hsu
Lee, Tsu-Tian
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Hopfield-based dynamic neural network;dynamic neural network;Lyapunov stability theory;indirect adaptive control
公開日期: 2009
摘要: In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-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. In addition. the tracking error can be attenuated to a desired level by selecting some parameters adequately. 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/11536/135614
ISBN: 978-3-642-03968-3
ISSN: 1865-0929
期刊: ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PROCEEDINGS
Volume: 43
起始頁: 336
結束頁: +
顯示於類別:會議論文