Adaptive backstepping control for a class of semistrict feedback nonlinear systems using neural networks

被引：12

作者：

Yang H. ^{[1
]}

Li Z. ^{[1
]}

机构：

[1] School of Automation, Beijing Institute of Technology

来源：

Journal of Control Theory and Applications | 2011年 / 9卷 / 2期

关键词：

Adaptive backstepping; Dynamic surface control; Radius-basis-function (RBF); Semistrict feedback form;

D O I：

10.1007/s11768-011-8162-2

中图分类号：

学科分类号：

摘要：

This paper addresses a neural adaptive backstepping control with dynamic surface control technique for a class of semistrict feedback nonlinear systems with bounded external disturbances. Neural networks (NNs) are introduced as approximators for uncertain nonlinearities and the dynamic surface control (DSC) technique is involved to solve the so-called "explosion of terms" problem. In addition, the NN is used to approximate the transformed unknown functions but not the original nonlinear functions to overcome the possible singularity problem. The stability of closed-loop system is proven by using Lyapunov function method, and adaptation laws of NN weights are derived from the stability analysis. Finally, a numeric simulation validates the results of theoretical analysis. © 2011 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

引用

页码：220 / 224

页数：4

共 12 条

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