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A nonlinear sliding mode control design approach based on neural network modelling
Author(s) -
Bhatti A. I.,
Spurgeon S. K.,
Lu X. Y.
Publication year - 1999
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/(sici)1099-1239(199906)9:7<397::aid-rnc412>3.0.co;2-0
Subject(s) - robustness (evolution) , nonlinear system , control theory (sociology) , sliding mode control , artificial neural network , backpropagation , computer science , control engineering , robust control , nonlinear control , control (management) , engineering , artificial intelligence , biochemistry , chemistry , physics , quantum mechanics , gene
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 © 1999 John Wiley & Sons, Ltd.