Reduced-Order Fault Detection Filter Design for Fuzzy Semi-Markov Jump Systems With Partly Unknown Transition Rates
This article deals with the fault detection problem for a class of Takagi-Sugeno (T-S) fuzzy semi-Markov jump systems (FSMJSs) with partly unknown transition rates (PUTRs) subject to output quantization by designing a reduced-order filter. First, a more general PUTRs model is constructed to describe...
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          | Published in | IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 12; pp. 7702 - 7713 | 
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| Main Authors | , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        New York
          IEEE
    
        01.12.2022
     The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 2168-2216 2168-2232  | 
| DOI | 10.1109/TSMC.2022.3163719 | 
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| Summary: | This article deals with the fault detection problem for a class of Takagi-Sugeno (T-S) fuzzy semi-Markov jump systems (FSMJSs) with partly unknown transition rates (PUTRs) subject to output quantization by designing a reduced-order filter. First, a more general PUTRs model is constructed to describe the situation that the information of some elements is completely unknown, where this model is affected simultaneously by PU information and time-varying parameter compared with the traditional PUTRs model. Second, we take full advantage of the reduced-order filter to address the fault detection problem for FSMJSs, in which the stochastic failure phenomenon is injected into the reduced-order filter. Besides, the logarithmic quantizer is employed to tackle the limited bandwidth problem in a communication channel. Consequently, the new sufficient conditions are developed based on the Lyapunov theory to obtain the desired reduced-order filter. Simulation results with respect to the tunnel diode circuit are provided to demonstrate the usefulness and availability of the established theoretical results. | 
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14  | 
| ISSN: | 2168-2216 2168-2232  | 
| DOI: | 10.1109/TSMC.2022.3163719 |