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 |