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PD-Type Iterative Learning Control for Uncertain Spatially Interconnected Systems

Author

Listed:
  • Longhui Zhou

    (Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China)

  • Hongfeng Tao

    (Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China)

  • Wojciech Paszke

    (Institute of Automation, Electronic and Electrical Engineering, University of Zielona Góra, 65-516 Zielona Góra, Poland)

  • Vladimir Stojanovic

    (Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, 36000 Kraljevo, Serbia)

  • Huizhong Yang

    (Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China)

Abstract

This paper puts forward a PD-type iterative learning control algorithm for a class of discrete spatially interconnected systems with unstructured uncertainty. By lifting and changing the variable of discrete space model, the uncertain spatially interconnected systems is converted into equivalent singular system, and the general state space model is derived in view of singular system theory. Then, the state error and output error information are used to design the iterative learning control law, transforming the controlled system into an equivalent repetitive process model. Based on the stability theory of repetitive process, sufficient condition for the stability of the system along the trial is given in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed algorithm is verified by the simulation of ladder circuits.

Suggested Citation

  • Longhui Zhou & Hongfeng Tao & Wojciech Paszke & Vladimir Stojanovic & Huizhong Yang, 2020. "PD-Type Iterative Learning Control for Uncertain Spatially Interconnected Systems," Mathematics, MDPI, vol. 8(9), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1528-:d:409986
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    References listed on IDEAS

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    1. Feng, Hongyan & Xu, Huiling & Xu, Shengyuan & Chen, Weimin, 2019. "Model reference tracking control for spatially interconnected discrete-time systems with interconnected chains," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 50-62.
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    Cited by:

    1. Lü, Shao-Yu & Jin, Xiao-Zheng & Wu, Xiao-Ming & Ding, Li-Jian & Chi, Jing, 2022. "Robust adaptive event-triggered fault-tolerant control for time-varying systems against perturbations and faulty actuators," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    2. Yingqi Lu & Maede Maftouni & Tairan Yang & Panni Zheng & David Young & Zhenyu James Kong & Zheng Li, 2023. "A novel disassembly process of end-of-life lithium-ion batteries enhanced by online sensing and machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2463-2475, June.
    3. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    4. Zejun Tong & Chun Zhang & Xiaotai Wu & Pengcheng Gao & Shuang Wu & Haoyu Li, 2023. "Economic Optimization Control Method of Grid-Connected Microgrid Based on Improved Pinning Consensus," Energies, MDPI, vol. 16(3), pages 1-31, January.

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