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Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes

Author

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  • Limin Wang
  • Yiteng Shen
  • Jingxian Yu
  • Ping Li
  • Ridong Zhang
  • Furong Gao

Abstract

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini–Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy.

Suggested Citation

  • Limin Wang & Yiteng Shen & Jingxian Yu & Ping Li & Ridong Zhang & Furong Gao, 2018. "Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(2), pages 324-343, January.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:2:p:324-343
    DOI: 10.1080/00207721.2017.1402215
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    Cited by:

    1. Yan Geng & Shouqin Wang & Xiaoe Ruan, 2022. "The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes," Mathematics, MDPI, vol. 10(13), pages 1-19, July.

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