Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/4730
Title: Distributed H-infinity filtering for polynomial nonlinear stochastic systems in sensor networks
Authors: Shen, B
Wang, Z
Hung, Y
Chesi, G
Keywords: Sensor networks;Distributed H1 filtering;Parameter-dependent linear matrix inequalitites;Polynomial systems;Stochastic systems;Sum of squares
Issue Date: 2010
Publisher: IEEE
Citation: IEEE Transactions on Industrial Electronics, Forthcoming, June 2010
Abstract: In this paper, the distributed H1 filtering problem is addressed for a class of polynomial nonlinear stochastic systems in sensor networks. For a Lyapunov function candidate whose entries are polynomials, we calculate its first- and second-order derivatives in order to facilitate the use of Itos differential role. Then, a sufficient condition for the existence of a feasible solution to the addressed distributed H1 filtering problem is derived in terms of parameter-dependent linear matrix inequalities (PDLMIs). For computational convenience, these PDLMIs are further converted into a set of sums of squares (SOSs) that can be solved effectively by using the semidefinite programming technique. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.
Description: Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://bura.brunel.ac.uk/handle/2438/4730
DOI: http://dx.doi.org/10.1109/TIE.2010.2053339
ISSN: 0278-0046
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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