A two-dimensional design of model predictive control for batch processes with two-dimensional (2D) dynamics using extended non-minimal state space structure. (September 2019)

• Record Type: Journal Article
• Authors: Wu, Sheng; Zhang, Ridong
• Is Part Of: Journal of process control. Volume 81(2019)
• Page Start: 172
• Page End: 189
• Publication Date: 2019-09
• Subjects: 2D sense -- Model predictive iterative learning control -- Enhanced model -- Batch process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.07.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A 2D extended non-minimal state space (ENMSS) model is introduced. An improved two-dimensional model predictive iterative learning control (2D-MPILC) strategy is proposed. More degrees of freedom are provided to tune the corresponding controller. Abstract: A modified two-dimensional (2D) structure based control strategy that incorporates model predictive control (MPC) and iterative learning control (ILC) in 2D sense is developed for batch processes in this article. To enhance the control effect of the conventional 2D model predictive iterative learning control (2D-MPILC), an improved non-minimal form of state space model is utilized in the developed method. Through employing such model, the dynamics of the closed-loop system can be regulated by tuning extra process variables. In other words, additional degrees of freedom are achieved in the novel controller design, and the modified control performance is expected for the proposed algorithm. The simulation results on two processes evaluate the effectiveness of the presented 2D-MPILC strategy finally.