Model-Free Adaptive Learning Control With Prescribed Performance of Nonlinear Systems

Yong-Sheng Ma; Wei-Wei Che; Zheng-Guang Wu

doi:10.1109/JAS.2026.125885

Volume 13 Issue 9

Sep. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(9): 2078-2087

Y.-S. Ma, W.-W. Che, and Z.-G. Wu, “Model-free adaptive learning control with prescribed performance of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2078–2087, Sep. 2026. doi: 10.1109/JAS.2026.125885

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Y.-S. Ma, W.-W. Che, and Z.-G. Wu, “Model-free adaptive learning control with prescribed performance of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2078–2087, Sep. 2026. doi: 10.1109/JAS.2026.125885

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Model-Free Adaptive Learning Control With Prescribed Performance of Nonlinear Systems

doi: 10.1109/JAS.2026.125885

Funds: This work was supported by the National Natural Science Foundation of China (62633004, 62273191, 62233015) and the Fundamental Research Funds for the Central Universities (N25GFZ013)

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Author Bio:
Yong-Sheng Ma received the B.S. degree in electrical engineering and automation from the Department of Automation, Qingdao University of Science and Technology in 2019, and the M.S. degree in control engineering from the Department of Automation, Qingdao University in 2022. He is currently a Ph.D. degree candidate in control science and engineering at the College of Automation, Beijing Institute of Technology. He is also with Northeastern University. His current research interests include data-driven control, multi-agent systems, distributed cooperative control, and the security of network systems

Wei-Wei Che (Member, IEEE) received the B.S. degree in mathematics and applied mathematics from Jinzhou Normal University in 2002, the M.S. degree in applied mathematics from Bohai University in 2005, and the Ph.D. degree in control engineering from Northeastern University in 2008. She was a Postdoctoral Fellow with the IEEE Department, Nanyang Technological University, Singapore, from 2008 to 2009. She was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, China, in 2015. She is currently a Professor with the College of Information Science and Engineering, Northeastern University. Her research interests include nonfragile control, quantization control, as well as fault-tolerant control and their applications to NCSs, MASs, and CPSs design

Zheng-Guang Wu (Member, IEEE) received the B.S. and M.S. degrees in mathematics from Zhejiang Normal University in 2004 and 2007, respectively, and the Ph.D. degree in control science and engineering from Zhejiang University in 2011. He is currently a Professor with the Institute of Cyber-Systems and Control, Zhejiang University. His current research interests include hybrid systems, Markov jump systems, sampled-data systems, fuzzy systems, multi-agent systems, Boolean control networks, stochastic systems, neural networks.He has authored or coauthored over 100 papers in refereed international journals. He was named a Highly Cited Researcher (Clarivate Analytics). He is a Member of IEEE and an invited Reviewer of Mathematical Review of the American Mathematical Society. He serves as an Associate Editor/Editorial Board Member for some international journals, such as the IEEE Transactions on Systems, Man and Cybernetics: Systems; Journal of The Franklin Institute; Neurocomputing; International Journal of Control, Automation, and Systems; IEEE Access; International Journal of Sensors, Wireless Communications and Control; and IEEE Control Systems Society Conference Editorial Board
Corresponding author: Wei-Wei Che, e-mail: cheweiwei@ise.neu.edu.cn
Received Date: 2025-10-26
Accepted Date: 2026-01-09

Abstract

Abstract

This paper proposes the data-driven prescribed performance adaptive learning control (DPPALC) algorithm to accomplish the prescribed performance control for nonlinear systems, which can ensure that the tracking error converges to the prescribed region in the prescribed time. Considering that system model is unavailable, a novel model learning algorithm is developed to equivalently represent the original system with a data model, in which the dynamic linearization technique is used to achieve this transformation. Then, the DPPALC algorithm is developed by the means of the transformed data equation. Compared with the existing methods, the main superiority of the presented learning algorithm is that the tracking error converges to the prescribed region within a given time by only using the system data. Two simulations are used to exemplify the devised DPPALC algorithm.
- Dynamic linearization technique,
- model-free adaptive control,
- nonlinear system,
- prescribed performance control

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References(34)

References

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Model-Free Adaptive Learning Control With Prescribed Performance of Nonlinear Systems

doi: 10.1109/JAS.2026.125885

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