Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Yunfeng Hu; Chong Zhang; Bo Wang; Jing Zhao; Xun Gong; Jinwu Gao; Hong Chen

doi:10.1109/JAS.2023.123603

Volume 11 Issue 2

Feb. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(2): 344-361

Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and H. Chen, “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603

Citation:

Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and H. Chen, “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603

Citation:

Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and H. Chen, “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603

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Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

doi: 10.1109/JAS.2023.123603

Funds: This work was supported by the National Natural Science Foundation of China (U21A20166), in part by the Science and Technology Development Foundation of Jilin Province (20230508095RC), in part by the Development and Reform Commission Foundation of Jilin Province (2023C034-3), and in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control

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Author Bio:
Yunfeng Hu received the M.S. degree in mathematics and the Ph.D. degree in control theory and control engineering from Jilin University, in 2008 and 2012, respectively. He is currently a Professor with the Department of Control Science and Engineering, Jilin University. His current research interests include model predictive control, nonlinear control, and robust control and applications in mechatronic systems

Chong Zhang received the B.S. and M.S. degrees from Changchun University of Technology, in 2018 and Jilin University, in 2021 respectively. Now, he is currently pursuing the Ph.D. degree in Jilin University. His research interests include iterative learning control, nonlinear control and neural network

Bo Wang received the B.S. and M.S. degrees from North University of China in 2019 and Jilin University, in 2022. Now, he is pursuing the PhD degree at Jilin University. His research interests include model predictive control, nonlinear control and applications in engine emission reduction

Jing Zhao received the Ph.D. degree in electromechanical engineering from University of Macau, Macao, China, in 2016. He is currently working with Department of Electromechanical Engineering, University of Macau, Macao, China. His research interests include dynamics and control, mechanism and machine theory, fluid mechanics, and finite element analysis

Xun Gong received the Ph.D. degree in control theory and control engineering from Jilin University, in 2016. He was a joint Ph.D. student and a Postdoctoral Researcher in the University of Michigan, USA, from 2013 to 2015 and 2016 to 2018, respectively. He is currently an Associate Professor with the School of Artificial Intelligence, Jilin University. His current research interests include model-based nonlinear control and optimal control, and control applications to automotive systems

Jinwu Gao received the B.Eng. degree from the Department of Automation Measurement and Control Engineering, Harbin Institute of Technology, in 2005, and the Ph.D. degree from the Department of Control Science and Engineering, Harbin Institute of Technology, in 2012. From 2012 to 2014, he was an Assistant Professor with Sun Yat-sen University. In July 2014, he held a post-doctoral position with the Department of Engineering and Applied Science, Sophia University, Japan. Since November 2016, he has been an Associate Professor at Jilin University. His research interests include control theory and application in automotive powertrain

Hong Chen (Fellow, IEEE) received the B.S. and M.S. degrees in process control from Zhejiang University, in 1983 and 1986, respectively, and the Ph.D. degree in system dynamics and control engineering from the University of Stuttgart, Germany, in 1997. In 1986, she joined the Jilin University of Technology. From 1993 to 1997, she was a Wissenschaftlicher Mitarbeiter with the Institut fuer Systemdynamik und Regelungstechnik, University of Stuttgart, Germany. From 2015 to 2019, she was the Director of the State Key Laboratory of Automotive Simulation and Control. In 2019, she joined as a Distinguished Professor with Tongji University. Since 1999, she has been a Professor with Jilin University and hereafter a Tang Aoqing Professor. Her current research interests include model predictive control, nonlinear control, and applications in mechatronic systems focusing on automotive systems
Corresponding author: Yunfeng Hu, e-mail: huyf@jlu.edu.cn; Xun Gong, e-mail: gongxun@jlu.edu.cn
Received Date: 2022-11-14
Accepted Date: 2023-03-31

Available Online: 2023-11-15

Abstract

Abstract

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.
- Adaptive control,
- control system synthesis,
- data-driven iterative learning control,
- neurocontroller,
- nonlinear discrete time systems

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References

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Novel data-driven iterative learning decoupling control for complex MIMO systems
ILC based on NT-ZNNs: global convergence, noise sensitivity reduction
The elimination of the assumption about initialization conditions

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

doi: 10.1109/JAS.2023.123603

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