Neural network robust adaptive control for a class of time delay uncertain nonlinear systems

被引：0
|
作者：
Mei, Rong [1 ,2 ]
Wu, Qing-Xian [1 ]
Jiang, Chng-Sheng [1 ]
机构：
[1] Automation College, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
[2] Criminal Investigation Department, Nanjing Forest Police College, Nanjing, 210042, China
来源：
International Journal of Innovative Computing, Information and Control | 2010年 / 6卷 / 03期
关键词：
Closed loop systems - Radial basis function networks - Uncertainty analysis - Adaptive control systems - Delay control systems - Robust control - Timing circuits - Nonlinear systems - Uncertain systems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a robust adaptive eontrol seheme based on neural network is proposed for a elass of time delay uneertain nonlinear systems. The system under study possesses a wide elass of nonlinear time-delayed input uncertainties and function uncertainties. The radial basis function (RBF) neural networks are introduced to approximate the unknown bounded continuous input uncertainties with appropriate weight value updated laws. Based on the output of RBF neural network, a robust adaptive control scheme is developed for the time delay uncertain system. To obtain the stable closed-loop system, the parameter updated laws are presented for the approximation error of RBF neural network and the uncertain parameters of time delayed item in the input uncertainty. Finally, an example is given to illustrate the effectiveness of the proposed control scheme. © 2010 ISSN 1349-4198.
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页码：931 / 940
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