Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model

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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a LV-NILMPC method based on the DFPLS model for trajectory tracking and process disturbance suppression in nonlinear batch processes. By integrating the T-S fuzzy model into the dynamic PLS inner model, the dynamic and nonlinear characteristics of the physical system are constructed. The method has a faster convergence rate and smaller tracking error compared to previous methods, and it is suitable for nonlinear, multivariable, and strong coupling batch processes.