Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117818
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Type: Journal article
Title: Reliable filtering of nonlinear Markovian jump systems: the continuous-time case
Author: Wu, Z.
Dong, S.
Shi, P.
Su, H.
Huang, T.
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019; 49(2):386-394
Publisher: IEEE
Issue Date: 2019
ISSN: 2168-2216
2168-2232
Statement of
Responsibility: 
Zheng-Guang Wu, Shanling Dong, Peng Shi, Hongye Su and Tingwen Huang
Abstract: This paper is concerned with the reliable ℒ ₂ - ℒ ∞ filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi-Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed ℒ ₂ - ℒ ∞ performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.
Keywords: Hidden Markovian model (HMM); Markov jump systems; nonsynchronous filter; sensor failures; Takagi–Sugeno (T–S) fuzzy systems
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030080407
DOI: 10.1109/TSMC.2017.2778282
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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