Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

被引：724

作者：

Zhang, T ^{[1
]}

Ge, SS ^{[1
]}

Hang, CC ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect Engn, Singapore 119260, Singapore

来源：

AUTOMATICA | 2000年 / 36卷 / 12期

关键词：

nonlinear systems; adaptive control; neural networks; Lyapunov stability;

D O I：

10.1016/S0005-1098(00)00116-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on adaptive control of strict-feedback nonlinear systems using multilayer neural networks (MNNs). By introducing a modified Lyapunov function, a smooth and singularity-free adaptive controller is firstly designed for a first-order plant. Then, an extension is made to high-order nonlinear systems using neural network approximation and adaptive backstepping techniques. The developed control scheme guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. In addition, the relationship between the transient performance and the design parameters is explicitly given to guide the tuning of the controller. One important feature of the proposed NN controller is the highly structural property which makes it particularly suitable for parallel processing in actual implementation. Simulation studies are included to illustrate the effectiveness of the proposed approach. (C) 2000 Elsevier Science Ltd. All rights reserved.

引用

页码：1835 / 1846

页数：12

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