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Fuzzy model‐based fault detection for Markov jump systems
Author(s) -
He Shuping,
Liu Fei
Publication year - 2008
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1380
Subject(s) - control theory (sociology) , robustness (evolution) , sensitivity (control systems) , fuzzy logic , fault detection and isolation , computer science , markov chain , nonlinear system , filter (signal processing) , mathematical optimization , mathematics , engineering , artificial intelligence , machine learning , control (management) , biochemistry , chemistry , physics , quantum mechanics , electronic engineering , actuator , computer vision , gene
The robust fault detection filter (RFDF) design problems are studied for nonlinear stochastic time‐delay Markov jump systems. By means of the Takagi–Sugeno fuzzy models, the fuzzy RFDF system and the dynamics of filtering error generator are constructed. Moreover, taking into account the sensitivity to faults while guaranteeing robustness against unknown inputs, the H ∞ filtering scheme is proposed to minimize the influences of the unknown inputs and another new performance index is introduced to enhance the sensitivity to faults. A sufficient condition is first established on the stochastic stability using stochastic Lyapunov–Krasovskii function. Then in terms of linear matrix inequalities techniques, the sufficient conditions on the existence of fuzzy RFDF are presented and proved. Finally, the design problem is formulated as a two‐objective optimization algorithm. Simulation results illustrate that the proposed RFDF can detect the faults shortly after the occurrences. Copyright © 2008 John Wiley & Sons, Ltd.