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IEEE/CAA Journal of Automatica Sinica

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M. Wang, H. Yan, J. Qiu, and W. Ji, “Fuzzy-model-based finite frequency fault detection filtering design for two-dimensional nonlinear systems,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124452
Citation: M. Wang, H. Yan, J. Qiu, and W. Ji, “Fuzzy-model-based finite frequency fault detection filtering design for two-dimensional nonlinear systems,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124452

Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems

doi: 10.1109/JAS.2024.124452
Funds:  This work was supported in part by the National Natural Science Foundation of China (Nos. 62373152, 62333005, U21B6001, 62073143, 62273121), in part by the Natural Science Funds for Excellent Young Scholars of Hebei Province in 2022 under Grant F2022202014, in part by Science and Technology Research Project of Colleges and Universities in Hebei Province under Grant BJ2020017, and in part by the China Postdoctoral Science Foundation under Grant 2022M711639 and 2023T160320
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  • This article studies the fault detection filtering design problem for Roesser type two-dimensional (2-D) nonlinear systems described by uncertain 2-D Takagi-Sugeno (T-S) fuzzy models. Firstly, fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited, based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to faults. It has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional non-frequency based filtering design approach. Then, with the proposed evaluation function and its threshold, a novel mixed finite frequency $ {\cal{H}}_{\infty}/{\cal{H}}_{-}$ fault detection algorithm is developed, based on which the fault can be immediately detected once the evaluation function exceeds the threshold. Finally, it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.

     

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