Dynamic R-parameter based integrated model predictive iterative learning control for batch processes. (January 2017)

• Record Type: Journal Article
• Authors: Jia, Li; Han, Chao; Chiu, Min-sen
• Is Part Of: Journal of process control. Volume 49(2017:Jan.)
• Page Start: 26
• Page End: 35
• Publication Date: 2017-01
• Subjects: Batch process -- Integrated learning control -- Iterative learning control (ILC) -- Model predictive control (MPC) -- Model identification -- Dynamic R-parameter; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2016.11.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: 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. Abstract: 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. As a result, 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. Moreover, both model identification and dynamic R-parameter are employed to eliminate the model-plant mismatch and make zero-error tracking possible. Next, the convergence and tracking performance of the proposed integrated model predictive learning control system are given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by one example.