Parameter identification for uncertain plants using \(H^ \infty\) methods
type: Item
instance of: scholarly article
title: Parameter identification for uncertain plants using \(H^ \infty\) methods
zbMATH Open document ID: 0838.93026
DOI: 10.1016/0005-1098(95)00073-6
author: Garry Didinsky
author: Zigang Pan
author: Tamer Başar
published in: Automatica
publication date: 1995-11-02T00:00:00Z
review text: The use of \(H^\infty\) filtering and cost-to-come methods are applied to worst case parameter identification in deterministic but uncertain plants that are linear in the unknown parameters. The approach consists in constructing a cost-to-come function which assists in the design of an ``optimal'' observer. Full state measurements are assumed. One variant uses pre-filtering of past states, inputs and disturbances to estimate the derivative information. Another solves two subproblems: Parameter identification based on the assumption of full state with derivative information and use of an estimator of noise perturbed state measurements. It is shown using singular perturbation methods that the identifier using noise perturbed measurements asymptotically converges to the unperturbed case. In this sense, the full state measurement without derivative information parameter identification problem is solved in an asymptotically optimal way. A numerical example is included.
reviewed by: Edward Boje
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93B36
zbMATH DE Number: 806663
zbMATH Keywords: \(H^ \infty\) filtering
zbMATH Keywords: worst case parameter identification
zbMATH Keywords: singular perturbation
MaRDI profile type: Publication
full work available at URL: 0005-1098(95)00073-6
OpenAlex ID: W2062218337
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description: scientific article; zbMATH DE number 806663