Robust \(H_\infty\) filtering for 2D stochastic system
type: Item
instance of: scholarly article
title: Robust \(H_\infty\) filtering for 2D stochastic system
zbMATH Open document ID: 1175.93222
author: Huijun Gao
author: James Lam
author: Shengyuan Xu
author: Changhong Wang
published in: Circuits, Systems, and Signal Processing
publication date: 2005-04-05T00:00:00Z
review text: The paper deals with the \(H_{\infty}\) filtering problem for an extended Fornasini-Marchesini local state-space model, established by introducing stochastic perturbations, which are governed by a white-noise signal and cause the system matrices to fluctuate above their deterministic nominal values. The design concerns the full-order and reduced-order filters, which guarantee the mean-square asymptotic stability of filtering error system and a prescribed \(H_{\infty}\) disturbance attenuation performance. Sufficient existence conditions in terms of linear matrix inequalities are derived and it is shown that for the filter design a convex optimization procedure can be performed. Extensions to more general cases where the system matrices contain uncertain parameters are provided, by including polytopic and norm-bounded characterizations in order to deal with parameter uncertainties. Finally a numerical example illustrates the usefulness of the proposed filter design procedures.
reviewed by: Mihail Voicu
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93B36
zbMATH DE Number: 2152923
zbMATH Keywords: 2D systems
zbMATH Keywords: \(H_{\infty}\) filtering
zbMATH Keywords: linear matrix inequality
zbMATH Keywords: robust filtering
zbMATH Keywords: stochastic perturbation
MaRDI profile type: Publication
full work available at URL: s00034-004-1121-0
OpenAlex ID: W2109464743
DOI: 10.1007/S00034-004-1121-0
description: scientific article; zbMATH DE number 2152923