Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12320
Title: Robust filtering for a class of nonlinear stochastic systems with probability constraints
Authors: Ma, L
Wang, Z
Lam, HK
Alsaadi, FE
Liu, X
Keywords: Probability constraint;Time-varying systems;Measurements degradation;Stochastic nonlinearity;Parameter uncertainty
Issue Date: 2016
Publisher: Pleiades Publishing
Citation: Automation and Remote Control, 77(1): pp. 37 - 54, (2016)
Abstract: This paper is concerned with the probability-constrained filtering problem for a class of time-varying nonlinear stochastic systems with estimation error variance constraint. The stochastic nonlinearity considered is quite general that is capable of describing several well-studied stochastic nonlinear systems. The second-order statistics of the noise sequence are unknown but belong to certain known convex set. The purpose of this paper is to design a filter guaranteeing a minimized upper-bound on the estimation error variance. The existence condition for the desired filter is established, in terms of the feasibility of a set of difference Riccati-like equations, which can be solved forward in time. Then, under the probability constraints, a minimax estimation problem is proposed for determining the suboptimal filter structure that minimizes the worst-case performance on the estimation error variance with respect to the uncertain second-order statistics. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed method.
URI: http://link.springer.com/article/10.1134%2FS0005117916010033
http://bura.brunel.ac.uk/handle/2438/12320
DOI: http://dx.doi.org/10.1134/S0005117916010033
ISSN: 0005-1179
Appears in Collections:Dept of Computer Science Research Papers

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