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

doi:10.1016/j.cjche.2018.06.006

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (8): 1713-1720.DOI: 10.1016/j.cjche.2018.06.006

• Selected Papers from the 28th Chinese Process Control Conference • Previous Articles Next Articles

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

Li Jia, Wendan Tan

Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China

Received:2017-10-03 Revised:2018-03-02 Online:2018-09-21 Published:2018-08-28
Contact: Li Jia,E-mail address:jiali@staff.shu.edu.cn
Supported by:
Supported by the National Natural Science Foundation of China (61374044), Shanghai Science Technology Commission (15510722100, 16111106300), and Shanghai Municipal Education Commission (14ZZ088).

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

Li Jia, Wendan Tan

Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China

通讯作者: Li Jia,E-mail address:jiali@staff.shu.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (61374044), Shanghai Science Technology Commission (15510722100, 16111106300), and Shanghai Municipal Education Commission (14ZZ088).

Abstract

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.

Key words: Model predictive control, Batch process, Just-in-time learning (JITL) model

摘要： 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.

关键词: Model predictive control, Batch process, Just-in-time learning (JITL) model

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.

Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1713-1720.

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

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

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