Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117715
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Type: Journal article
Title: Networked fault detection for Markov jump nonlinear systems
Author: Dong, S.
Wu, Z.
Shi, P.
Karimi, H.
Su, H.
Citation: IEEE Transactions on Fuzzy Systems, 2018; 26(6):3368-3378
Publisher: IEEE
Issue Date: 2018
ISSN: 1063-6706
1941-0034
Statement of
Responsibility: 
Shanling Dong, Zheng-Guang Wu, Peng Shi, Hamid Reza Karimi and Hongye Su
Abstract: This paper deals with the problem of dissipativity-based asynchronous fault detection (FD) for Takagi-Sugeno fuzzy Markov jump systems with network data dropouts. It is assumed that data dropouts happen intermittently from the plant to the FD filter, which is described by Bernoulli process. The hidden Markov model is employed to describe the asynchronous phenomenon between the plant and filter. Based on Lyapunov theory, a sufficient condition is developed to guarantee that the FD system is stochastically stable with strictly dissipative performance. By choosing an appropriate Lyapunov function with the slack matrix technique and Finsler's Lemma, two approaches are proposed to compute filter gains by solving linear matrix inequalities. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed design methods.
Keywords: Data dropouts; fault detection (FD); fuzzy Markov jump systems (MJSs); hidden Markov model (HMM)
Rights: © 2018 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: 0030086622
DOI: 10.1109/TFUZZ.2018.2826467
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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