Model predictive control of batch processes based on two-dimensional integration frame. (May 2018)

• Record Type: Journal Article
• Authors: Han, Chao; Jia, Li; Peng, Daogang
• Is Part Of: Nonlinear analysis. Volume 28(2018)
• Page Start: 75
• Page End: 86
• Publication Date: 2018-05
• Subjects: Batch process -- Integrated model predictive control -- Model identification -- Dynamic R-parameter -- Iterative learning control; Nonlinear functional analysis -- Periodicals; Analyse fonctionnelle non linéaire -- Périodiques; Nonlinear functional analysis; Periodicals; 515.7248
• DOI: 10.1016/j.nahs.2017.11.002
• Languages: English
• ISSNs: 1751-570X

Abstract

Abstract: A novel integrated model predictive control (MPC) strategy for batch processes is proposed in this paper. Both batch-axis and time-axis information are integrated into a two-dimensional control frame. The control law is obtained through the solution of a MPC optimization with time-varying prediction horizon, which leads to superior tracking performance and robustness against disturbance and uncertainty. Moreover, both model identification and dynamic R-parameter are employed to compensate the model-plant mismatch and make zero-error tracking possible. Next, the convergence analysis and tracking performance of the proposed integrated model predictive learning control system are described and proved strictly. Lastly, the effectiveness of the proposed method is verified by an example.