A Novel Model Free Adaptive Fuzzy Control for Discrete-Time T-S Fuzzy Systems With Local Nonlinear Models

Xiaodong Bu; Shangtai Jin; Xisheng Dai; Zhongsheng Hou

doi:10.1109/JAS.2025.125810

Volume 13 Issue 5

May 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 19.2, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(5): 1207-1216

X. Bu, S. Jin, X. Dai, and Z. Hou, “A novel model free adaptive fuzzy control for discrete-time T-S fuzzy systems with local nonlinear models,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1207–1216, May 2026. doi: 10.1109/JAS.2025.125810

Citation:

X. Bu, S. Jin, X. Dai, and Z. Hou, “A novel model free adaptive fuzzy control for discrete-time T-S fuzzy systems with local nonlinear models,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1207–1216, May 2026. doi: 10.1109/JAS.2025.125810

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A Novel Model Free Adaptive Fuzzy Control for Discrete-Time T-S Fuzzy Systems With Local Nonlinear Models

doi: 10.1109/JAS.2025.125810

Funds: This work was supported in part by the Beijing Municipal Natural Science Foundation (4262063), the National Natural Science Foundation of China (62363002)

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Author Bio:
Xiaodong Bu received the B.S. degree in electrical engineering and automation from Henan University of Urban Construction in 2018, and the M.S. degree in control engineering from Guangxi University of Science and Technology in 2023. He is currently a Ph.D. degree candidate in control science and engineering at Beijing Jiaotong University. His research interests include data-driven control, model-free adaptive control and networked control systems

Shangtai Jin received the B.S. degree in automation and the M.S. and Ph.D. degrees in traffic information engineering and control from Beijing Jiaotong University in 1999, 2004, and 2009, respectively. From 2014 to 2015, he was a Visiting Researcher with Nanyang Technological University, Singapore. He is currently an Associate Professor with Beijing Jiaotong University. His current research interests include model-free adaptive control, data driven control, learning control, and intelligent transportation systems

Xisheng Dai (Member, IEEE) received the Ph.D. degree in control theory and control engineering from the South China University of Technology in 2010. He is currently a Professor with the School of Automation, Guangxi University of Science and Technology. His current research interests include iterative learning control of distributed parameter systems and stochastic systems control

Zhongsheng Hou (Fellow, IEEE) received the B.S. and M.S. degrees in applied mathematics from the Jilin University of Technology in 1983 and 1988, respectively, and the Ph.D. degree in control theory and applications from Northeastern University in 1994. From 1997 to 2018, he was a Distinguished Professor and the Head of the Department of Automatic Control with Beijing Jiaotong University. He is currently the Chair Professor with the School of Automation, Qingdao University. His research interests include data-driven control, model-free adaptive control, learning control, and intelligent transportation systems. Dr. Hou is the Founding Director of the Technical Committee on Data-Driven Control, Learning and Optimization, Chinese Association of Automation (CAA). He is a Fellow of CAA. He is an International Federation of Automatic Control Technical Committee Member of Adaptive and Learning Systems and Transportation Systems
Corresponding author: Shangtai Jin, e-mail: shtjin@bjtu.edu.cn
Received Date: 2025-03-28
Revised Date: 2025-05-15
Accepted Date: 2025-08-06

Abstract

Abstract

This paper presents a model-free adaptive fuzzy control (MFAFC) scheme for discrete-time Takagi-Sugeno (T-S) fuzzy systems with local nonlinear models. First, the T-S fuzzy system is transformed into a linearized model using a dynamic linearization technique. Then, a model-free adaptive control scheme is developed for T-S fuzzy systems. Next, a rigorous convergence analysis of the tracking error is carried out using the contraction mapping theory. Finally, to validate the theoretical results, the scheme is tested by two numerical simulations and a mass-spring damper mechanical system. The results show that the proposed MFAFC strategy is effective in ensuring that the system output tracks the desired trajectory.
- Data-driven control,
- model-free adaptive fuzzy control (MFAFC),
- tracking control,
- Takagi-Sugeno (T-S) fuzzy system

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References(35)

References

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