A Dynamic Event-Triggered Fuzzy Hi/H∞ Optimization Approach to Fault Detection for T-S Fuzzy Systems

Xiaoqiang Zhu; Linlin Li; Maiying Zhong; Jingzhong Fang; Jiahong Xu

doi:10.1109/JAS.2026.125891

Volume 13 Issue 9

Sep. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(9): 2088-2098

X. Zhu, L. Li, M. Zhong, J. Fang, and J. Xu, “A dynamic event-triggered fuzzy Hi/H∞ optimization approach to fault detection for T-S fuzzy systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2088–2098, Sep. 2026. doi: 10.1109/JAS.2026.125891

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X. Zhu, L. Li, M. Zhong, J. Fang, and J. Xu, “A dynamic event-triggered fuzzy H_i/H_∞ optimization approach to fault detection for T-S fuzzy systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2088–2098, Sep. 2026. doi: 10.1109/JAS.2026.125891

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A Dynamic Event-Triggered Fuzzy H_i/H_∞ Optimization Approach to Fault Detection for T-S Fuzzy Systems

doi: 10.1109/JAS.2026.125891

Funds: This paper was supported in part by the National Natural Science Foundation of China (62233012, 62322303, 62273214)

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Author Bio:
Xiaoqiang Zhu received the B.E. degree in the College of Electrical Engineering and Automation, Shandong University of Science and Technology in 2016. He is currently a Ph.D. candidate in detection technology and automatic equipment at the College of Electrical Engineering and Automation, Shandong University of Science and Technology. His current research interests include the theory and applications of fault diagnosis and fault tolerant control

Linlin Li (Senior Member, IEEE) received the B.E. degree in automation from Xi’an Jiaotong University in 2008 and the M.E. degree in mechanical systems and control from Peking University in 2011, the Ph.D. degree in automation from the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, in 2015. She is now a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her research interests include fault diagnosis and fault tolerant control, fuzzy control and estimation for nonlinear systems

Maiying Zhong received the Ph.D. degree in control theory and control engineering from the Northeastern University in 1999. From 2000 to 2001, she was a Visiting Scholar at the University of Applied Sciences Lausitz, Germany. From 2002 to July 2008, she was a Professor of the School of Control Science and Engineering at Shandong University. From 2006 to 2007, she was a Postdoctoral Researcher Fellow with the Central Queensland University, Australia. From 2009 to 2016, she was a Professor at Beihang University. In March 2016, she joined Shandong University of Science and Technology, where she is currently a Professor with the College of Electrical Engineering and Automation. Her research interests are model based fault diagnosis, fault tolerant systems and their applications. She is a Fellow of the Chinese Association of Automation (CAA)

Jingzhong Fang received the B.E. degree in automation from Shandong University of Science and Technology in 2020, and the M.S. degree in data science and analytics from Brunel University London, UK, in 2021. He is currently a Ph.D. candidate in computer science at Brunel University London, UK. His research interests include data analysis and deep learning techniques

Jiahong Xu received the M.S. degree in applied mathematics from the College of Mathematical Sciences, Bohai University in 2023. She is currently a Ph.D. candidate in control science and engineering at the College of Electrical Engineering and Automation, Shandong University of Science and Technology. Her current research interests include the theory and applications of fault diagnosis
Corresponding author: Linlin Li, e-mail: linlin.li@ustb.edu.cn; Maiying Zhong, e-mail: myzhong@buaa.edu.cn
Received Date: 2025-11-07
Accepted Date: 2026-01-09

Abstract

Abstract

This paper investigates the problem of dynamic event-triggered optimal fault detection (FD) for Takagi-Sugeno (T-S) fuzzy systems. Aiming at conserving limited communication resources, a dynamic event-triggering mechanism is implemented to transmit the system measurement to the remote fault detector. Based on it, a dynamic event-triggered T-S fuzzy fault detection filter (FDF) is constructed so that the generated residual is completely decoupled from the event-triggered transmission error. Subsequently, the design of T-S fuzzy FDF is formulated as an $H_i/H_{\infty}$ optimization problem such that an optimal tradeoff can be achieved between the robustness against unknown input and the sensitivity to fault. Different from existing linear matrix inequality-based event-triggered FD approaches for T-S fuzzy systems, the optimal solution is obtained by recursively computing the Riccati equations, allowing for the independent implementation of the event generator and the $H_i/H_{\infty}$-FDF. A three-phase induction motor system is employed to illustrate the effectiveness of the proposed method.
- Dynamic event-triggering mechanism,
- fault detection (FD),
- H_i/H_∞ optimization,
- Takagi-Sugeno (T-S) fuzzy systems

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References

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A Dynamic Event-Triggered Fuzzy Hi/H∞ Optimization Approach to Fault Detection for T-S Fuzzy Systems

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A Dynamic Event-Triggered Fuzzy H_i/H_∞ Optimization Approach to Fault Detection for T-S Fuzzy Systems