Adaptive control of nonlinear dynamic systems using \(\theta\)-adaptive neural networks
type: Item
instance of: scholarly article
title: Adaptive control of nonlinear dynamic systems using \(\theta\)-adaptive neural networks
zbMATH Open document ID: 0913.93041
DOI: 10.1016/S0005-1098(97)00130-1
author: Ssu-Hsin Yu
author: A. M. Annaswamy
published in: Automatica
publication date: 1999-06-07T00:00:00Z
review text: For dynamic systems with nonlinear parameterization, the adaptive control problem is addressed by designing a neural-network-based controller with a particular architecture. The structure of the controller is derived from the available physical model, whereas the parameters are estimated by a neural network. It is shown that the proposed adaptive controller ensures the closed-loop stability of the overall system. The neural network training is approached as an optimization problem with constraints representing sufficient conditions for the closed-loop stability. This constrained problem is eventually reduced to an unconstrained one, which can be solved by various optimization methods. Simulation experiments allow a comparison of the results obtained by the procedure proposed in this paper with other methods.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93D21
zbMATH DE Number: 1192932
zbMATH Keywords: constrained optimization
zbMATH Keywords: stabilization
zbMATH Keywords: nonlinear parameterization
zbMATH Keywords: adaptive control
zbMATH Keywords: neural network
reviewed by: Octavian Pastravanu
MaRDI profile type: Publication
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full work available at URL: s0005-1098(97)00130-1
OpenAlex ID: W1996035335
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description: scientific article; zbMATH DE number 1192932