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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6337

Title: Fuzzy-model-based robust fault detection with stochastic mixed time-delays and successive packet dropouts
Authors: Dong, H
Wang, Z
Lam, J
Gao, H
Keywords: Discrete-time fuzzy systems
Fault detection
Networked control systems (NCSs)
Packet dropouts
Randomly occurring mixed time delays
Publication Date: 2012
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man, and Cybernetics - Part B, 42(2): 365 - 376, Apr 2012
Abstract: This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi–Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.
Description: This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEE
Sponsorship: This work was supported in part by the National Natural Science Foundation of China under Grant 61028008, 60825303, 61004067, National 973 Project under Grant 2009CB320600, the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University), Ministry of Education, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the University of Hong Kong under Grant HKU/CRCG/200907176129 and the Alexander von Humboldt Foundation of Germany.
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6020813&tag=1
http://bura.brunel.ac.uk/handle/2438/6337
DOI: http://dx.doi.org/10.1109/TSMCB.2011.2163797
ISSN: 1083-4419
Appears in Collections:Information Systems and Computing
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