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The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

Author

Listed:
  • Yan Geng

    (Department of Applied Mathematics, School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China)

  • Shouqin Wang

    (Department of Applied Mathematics, School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China)

  • Xiaoe Ruan

    (Department of Applied Mathematics, School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

For multi-phase batch processes with different dimensions whose dynamics can be described as a linear discrete-time-invariant system in each phase, a data-driven optimal ILC was explored using multi-operation input and output data that subordinate a tracking performance criterion. An iterative learning identification was constructed to estimate the system Markov parameters by minimizing the evaluation criterion that consists of the residual of the real outputs from the predicted outputs and two adjacent identifications. Meanwhile, the estimated Markov parameters matrix was embedded into the learning control process in the form of an interaction. By virtue of inner product theory, the monotonic descent of the estimation error was derived, which does not restrict the weighting factor at all. Furthermore, algebraic derivation demonstrates that the tracking is strictly monotonically convergent if the estimation error falls within an appropriate domain. Numerical simulations were carried out to illustrate the validity and the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2304-:d:853863
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    References listed on IDEAS

    as
    1. Jian Liu & Yamiao Zhang & Xiaoe Ruan, 2019. "Iterative learning control for a class of uncertain nonlinear systems with current state feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(10), pages 1889-1901, July.
    2. 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.
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