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Stable linearization using multilayer neural networks

Delgado, A.; Kambhampati, Chandrasekhar; Warwick, K.

Authors

A. Delgado

Chandrasekhar Kambhampati C.Kambhampati@hull.ac.uk

K. Warwick



Abstract

The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedback coefficients that linearize a nonlinear control affine plant. The stability of the linearizing closed loop can be guaranteed if the autonomous plant is asymptotically stable and the state feedback is bounded.

Start Date Sep 2, 1996
Publication Date 1996
Journal IEE Conference Publication
Print ISSN 0537-9989
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 427
Issue 1
Pages 194 - 198
ISBN 0852966660
APA6 Citation Delgado, A., Kambhampati, C., & Warwick, K. (1996). Stable linearization using multilayer neural networks. https://doi.org/10.1049/cp%3A19960551
DOI https://doi.org/10.1049/cp%3A19960551
Keywords Asymptotic stability; Multilayer perceptrons; Feedforward neural nets; Neurocontrollers; State feedback; Linearisation techniques; Closed loop systems; Nonlinear control systems