Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model. (November 2022)

• Record Type: Journal Article
• Authors: Che, Yinping; Zhao, Zhonggai; Wang, Zhiguo; Liu, Fei
• Is Part Of: Journal of process control. Volume 119(2022)
• Page Start: 1
• Page End: 12
• Publication Date: 2022-11
• Subjects: Nonlinear batch process -- T-S model -- Dynamic fuzzy PLS modeling method -- Iterative learning -- Model predictive control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.09.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective. Highlights: We extend the iterative learning model predictive control to nonlinear processes. Partial least squares is employed to decouple the strongly coupled system. The controller is designed in the latent variable space rather than the original space. It has smaller tracking error and faster convergence speed than other methods.