Neural Network Based Variable Structure Control for Nonlinear Discrete Systems
Liu, G.P., Kardikamanathan, V. and Billings, S.A.
(1996)
Neural Network Based Variable Structure Control for Nonlinear Discrete Systems.
Research Report.
ACSE Research Report 626
.
Department of Automatic Control and Systems Engineering
Abstract
Neural network based variable structure control is proposed for the design of nonlinear discrete systems. Sliding mode control is used to provide good stability and robustness performance for nonlinear systems. An affine nonlinear neural predictor is introduced to predict the outputs of the nonlinear process and to make the variable structure control algorithm simple and easy to implement. When the predictor model is inaccurate, variable structure control with sliding modes is used to improve the stability of the system. A recursive weight learning algorithm for the neural networks based affine nonlinear predictor is also developed and the convergence of both the weights and the estimation error is analysed.
Metadata
Item Type:
Monograph
Authors/Creators:
Liu, G.P.
Kardikamanathan, V.
Billings, S.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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