Latent variable iterative learning model predictive control for multivariable control of batch processes. (October 2020) - Li, Xinwei; Zhao, Zhonggai; Liu, Fei
Abstract: A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC . Highlights: A novel iterative learning MPC strategy which is achieved in a latent variable space is proposed for trajectory tracking and disturbance rejection of batch processes. The advantages of latent variables in modeling and controller design are exploited through the implementation of an iterative learning MPC controller. The convergence of LV-ILMPC is proven. The proposed method is illustrated by its application in numerical simulations and a nonlinear boiler system.