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Title: Fault detection for markovian jump systems with sensor saturations and randomly varying nonlinearities
Authors: Dong, H
Wang, Z
Gao, H
Keywords: Fault detection;Markovian jumping systems;Incomplete knowledge of transition probabilities;Optimized filter;Randomly varying nonlinearities;Sensor saturation
Issue Date: 2012
Publisher: IEEE
Citation: IEEE Transactions on Circuits and Systems I: Regular Papers, 59(10): 2354 - 2362, Oct 2012
Abstract: This paper addresses the fault detection problem for discrete-time Markovian jump systems with incomplete knowledge of transition probabilities, randomly varying nonlinearities and sensor saturations. For the Markovian mode jumping, the transition probability matrix is allowed to have partially unknown entries, while the cases with completely known or completely unknown transition probabilities are also investigated as two special cases. The randomly varying nonlinearities and the sensor saturations are introduced to reflect the limited capacity of the communication networks resulting from the noisy environment, probabilistic communication failures, measurements of limited amplitudes, etc. Two energy norm indices are used for the fault detection problem in order to account for, respectively, the restraint of disturbance and the sensitivity of faults. The purpose of the problem addressed is to design an optimized fault detection filter such that 1) the fault detection dynamics is stochastically stable; 2) the effect from the exogenous disturbance on the residual is attenuated with respect to a minimized H∞-norm; and 3) the sensitivity of the residual to the fault is enhanced by means of a maximized H∞-norm. The characterization of the gains of the desired fault detection filters is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a simulation example is employed to show the effectiveness of the fault detection filtering scheme proposed in this paper.
Description: This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEE.
ISSN: 1549-8328
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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