Iterative learning control optimization strategy for feedback control systems with varying tasks

Fangmei Chen; Hongfeng Tao; Zhihe Zhuang; Wojciech Paszke; Vladimir Stojanovic; Fangmei Chen; Hongfeng Tao; Zhihe Zhuang; Wojciech Paszke; Vladimir Stojanovic

doi:10.3934/mmc.2025022

Mathematical Modelling and Control

2025, Volume 5, Issue 3: 321-337. doi: 10.3934/mmc.2025022

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Research article

Iterative learning control optimization strategy for feedback control systems with varying tasks

1.
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
2.
Institute of Automation, Electronic and Electrical Engineering, University of Zielona Góra, Zielona Góra, 65-417, Poland
3.
Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, Kraljevo 36000, Serbia

Received: 12 July 2025 Revised: 09 August 2025 Accepted: 18 August 2025 Published: 25 September 2025

Iterative learning control (ILC) combined with feedback control is a common approach to repetitive systems with external disturbances, as it enables high tracking performance and guarantees time-domain stability. However, the variation of the reference trajectory in practical repetitive operations often degrades the control performance. To this end, this paper develops a feedback-based ILC to transfer the experience of repetitively operating a certain task to a brand new task without restriction on its time duration. This two-dimensional (2-D) design employs a parallel structure, where the ILC and the feedback controller are designed separately to achieve performance optimization. Then, the feedback plus feedforward controller is integrated into a new feedback controller with learning-based parameters. The convergence and robustness analysis of the design is given. Finally, numerical simulation experiments of a DC motor position control system verify the proposed scheme's effectiveness and robustness.
- iterative learning control,
- feedback control,
- parallel structure,
- performance optimization,
- varying task
Citation: Fangmei Chen, Hongfeng Tao, Zhihe Zhuang, Wojciech Paszke, Vladimir Stojanovic. Iterative learning control optimization strategy for feedback control systems with varying tasks[J]. Mathematical Modelling and Control, 2025, 5(3): 321-337. doi: 10.3934/mmc.2025022

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Abstract

Iterative learning control (ILC) combined with feedback control is a common approach to repetitive systems with external disturbances, as it enables high tracking performance and guarantees time-domain stability. However, the variation of the reference trajectory in practical repetitive operations often degrades the control performance. To this end, this paper develops a feedback-based ILC to transfer the experience of repetitively operating a certain task to a brand new task without restriction on its time duration. This two-dimensional (2-D) design employs a parallel structure, where the ILC and the feedback controller are designed separately to achieve performance optimization. Then, the feedback plus feedforward controller is integrated into a new feedback controller with learning-based parameters. The convergence and robustness analysis of the design is given. Finally, numerical simulation experiments of a DC motor position control system verify the proposed scheme's effectiveness and robustness.

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