Dynamic R-parameter based integrated model predictive iterative learning control for batch processes

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• 作者：Li Jia^a ; ^{jiali@staff.shu.end.cn} ; Chao Han^a ; Min-sen Chiu^b ; ^{checms@nus.edu.sg}
• 关键词：Batch process ; Integrated learning control ; Iterative learning control (ILC) ; Model predictive control (MPC) ; Model identification ; Dynamic R-parameter
• 刊名：Journal of Process Control
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：49
• 期：Complete
• 页码：26-35
• 全文大小：1414 K
• 卷排序：49

文摘

A novel integrated model predictive iterative learning control (MPILC) strategy with dynamic R-parameter for batch processes is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame, namely the iterative learning controller (ILC) in the domain of batch-axis, while a model predictive controller (MPC)with time-varying prediction horizon in the domain of time-axis. The operation policy of batch process can be regulated during one batch, which leads to superior tracking performance and better robustness against disturbance and uncertainty. Both model identification and dynamic R-parameter are employed to eliminate the model-plant mismatch and make zero-error tracking possible. The convergence and tracking performance of the proposed integrated model predictive learning control system are given rigorous description and proof.