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Title: Robust filtering with stochastic nonlinearities and multiple missing measurements
Authors: Wei, G
Wang, Z
Shu, H
Keywords: Stochastic systems
Nonlinear systems
Uncertain systems
Missing measurements
Publication Date: 2009
Publisher: Elsevier
Citation: Automatica, 45(3): 836-841, Mar 2009
Abstract: This paper is concerned with the filtering problem for a class of discrete-time uncertain stochastic nonlinear time-delay systems with both the probabilistic missing measurements and external stochastic disturbances. The measurement missing phenomenon is assumed to occur in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval . Such a probabilistic distribution could be any commonly used discrete distribution over the interval . The multiplicative stochastic disturbances are in the form of a scalar Gaussian white noise with unit variance. The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing, stochastic disturbances, norm-bounded uncertainties as well as stochastic nonlinearities, the error dynamics of the filtering process is exponentially mean-square stable. By using the linear matrix inequality (LMI) method, sufficient conditions are established that ensure the exponential mean-square stability of the filtering error, and then the filter parameters are characterized by the solution to a set of LMIs. Illustrative examples are exploited to show the effectiveness of the proposed design procedures.
Description: This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier Ltd
Sponsorship: This work was supported in part by the Shanghai Natural Science Foundation under Grant 07ZR14002, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, an International Joint Project sponsored by the Royal Society of the UK, the Nuffield Foundation of the UK under Grant NAL/00630/G and the Alexander von Humboldt Foundation of Germany.
ISSN: 0005-1098
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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