Just-in-time learning based integrated MPC-ILC control for batch processes

Li Jia; Wendan Tan

doi:10.1016/j.cjche.2018.06.006

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Just-in-time learning based integrated MPC-ILC control for batch processes

Selected Papers from the 28th Chinese Process Control Conference | Updated：2026-01-06

- Just-in-time learning based integrated MPC-ILC control for batch processes
- Chinese Journal of Chemical Engineering Vol. 26, Issue 8, Pages: 1713-1720(2018)
- Affiliations：
  
  Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University,Shanghai,China,200072
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.06.006
  CLC：
- Published：2018
- Accepted：
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Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes[J]. Chin.J.Chem.Eng., 2018, 26(8): 1713-1720.
DOI：

Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes[J]. Chin.J.Chem.Eng., 2018, 26(8): 1713-1720. DOI： 10.1016/j.cjche.2018.06.006.

摘要

Abstract

Considering the two-dimension (2D) characteristic and the unknown optimal trajectory problem of the batch processes

an integrated model predictive control-iterative learning control (MPC-ILC) for batch processes is proposed in this paper. Firstly

the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea

which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory

both time-varying prediction horizon and end product quality control are employed. Moreover

an integrated 2D just-in-time learning (JITL) model is used to improve the predictive accuracy. Furthermore

rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.

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