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

Meng Wang; Huaicheng Yan; Jianbin Qiu; Wenqiang Ji

doi:10.1109/JAS.2024.124452

Volume 11 Issue 10

Oct. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(10): 2099-2110

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, vol. 11, no. 10, pp. 2099–2110, Oct. 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, vol. 11, no. 10, pp. 2099–2110, Oct. 2024. doi: 10.1109/JAS.2024.124452

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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 (62373152, 62333005, U21B6001, 62073143, 62273121), in part by the Natural Science Funds for Excellent Young Scholars of Hebei Province in 2022 (F2022202014), in part by Science and Technology Research Project of Colleges and Universities in Hebei Province (BJ2020017), and in part by the China Postdoctoral Science Foundation (2022M711639, 2023T160320)

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Author Bio:
Meng Wang received the B.Eng. degree in automation from Northeastern University at Qinhuangdao, in 2011, and the M.Eng. degree in control science and engineering from Harbin Institute of Technology, in 2013, and the Ph.D. degree from the Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China, in 2018. He is currently an Associate Professor at the School of Information Science and Engineering, East China University of Science and Technology. His research interests include robust control and filtering, fuzzy systems and control, and their engineering applications

Huaicheng Yan received the B.Sc. degree in automatic control from Wuhan University of Technology, in 2001, and the Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology, in 2007. In 2011, he was a Research Fellow with the University of Hong Kong, Hong Kong, China, for three months, and also a Research Fellow with the City University of Hong Kong, Hong Kong, China, in 2012, for six months. Currently, he is a Full Professor with the School of Information Science and Engineering, East China University of Science and Technology. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, International Journal of Robotics and Automation, and IEEE Open Journal of Circuits and Systems.His research interests include networked control systems, multi-agent systems and robotics

Jianbin Qiu (Fellow, IEEE) received the B.Eng. and Ph.D. degrees in mechanical and electrical engineering from the University of Science and Technology of China, in 2004 and 2009, respectively. He also received the Ph.D. degree in mechatronics engineering from the City University of Hong Kong, Hong Kong, China, in 2009.He is currently a Full Professor at the School of Astronautics, Harbin Institute of Technology. He was an Alexander von Humboldt Research Fellow at the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Germany. His current research interests include intelligent and hybrid control systems, signal processing, and robotics.Prof. Qiu is a Fellow of IEEE and serves as the Chair of the IEEE Industrial Electronics Society Harbin Chapter, China. He is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Informatics

Wenqiang Ji received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology (HIT). He is currently a Professor in the School of Artificial Intelligence, Hebei University of Technology. His main research interests include modeling and control of fuzzy systems, nonlinear systems, sampled-data control and filtering design, robust control, and sliding mode control
Corresponding author: Meng Wang, e-mail: mengwang@ecust.edu.cn
Received Date: 2024-03-01
Accepted Date: 2024-03-26

Available Online: 2024-06-20

Abstract

Abstract

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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References(42)

References

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

doi: 10.1109/JAS.2024.124452

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