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Variable neural networks for adaptive control of nonlinear systems

Liu, G.P.P., Kadirkamanathan, V. and Billings, S.A. (1999) Variable neural networks for adaptive control of nonlinear systems. IEEE Transactions on Systems Man and Cybernetics Part C: Applications and Reviews, 29 (1). pp. 34-43. ISSN 1094-6977

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Abstract

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: adaptive control, neural networks, nonlinear systems, radial basis functions
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 02 Dec 2005
Last Modified: 05 Jun 2014 13:52
Published Version: http://dx.doi.org/10.1109/5326.740668
Status: Published
Refereed: Yes
Identification Number: 10.1109/5326.740668
URI: http://eprints.whiterose.ac.uk/id/eprint/798

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