Stable adaptive neural control scheme for nonlinear systems

被引：1182
|
作者：
Polycarpou, MM
机构：
[1] Department of Electrical and Computer Engineering, University of Cincinnati, Cincinnati
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1996年 / 41卷 / 03期
关键词：
D O I：
10.1109/9.486648
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Based on the Lyapunov synthesis approach, several adaptive neural control schemes have been developed during the last few years. So far, these schemes have been applied only to simple classes of nonlinear systems. This paper develops a design methodology that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually made. One such assumption is the requirement of a known bound on the network reconstruction error. The overall adaptive scheme is shown to guarantee semiglobal uniform ultimate boundedness. The proposed feedback control law is a smooth function of the state.
引用
收藏
页码：447 / 451
页数：5
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