A nonlinear sliding mode control design approach based on neural network modelling

被引：1

作者：

Bhatti, AI ^{[1
]}

Spurgeon, SK ^{[1
]}

Lu, XY ^{[1
]}

机构：

[1] Univ Leicester, Dept Engn, Leicester LE1 7RH, Leics, England

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 1999年 / 9卷 / 07期

关键词：

neural network models; generalized controller canonical form; sliding mode control; uncertainty bounds;

D O I：

10.1002/(SICI)1099-1239(199906)9:7<397::AID-RNC412>3.0.CO;2-0

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A complete nonlinear framework for the modelling and robust control of nonlinear systems is proposed. The use of neural networks for continuous time modelling to obtain a certain nonlinear canonical form is investigated. The model obtained is used with recently proposed dynamic sliding mode controller design methods. The robustness bounds needed for controller design are determined from modelling errors. A modified version of the backpropagation theorem is also introduced. Copyright (C) 1999 John Wiley & Sons, Ltd.

引用

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页码：397 / 423

页数：27

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