Latent variable point-to-point iterative learning model predictive control via reference trajectory updating

A latent variable point-to-point iterative learning model predictive control algorithm (LV-PTP-ILMPC) that is combined with the reference trajectory updating strategy is proposed in this paper. It is different from the traditional point-to-point iterative learning model predictive control (PTP-ILMPC), which is developed in the original variable space to track a fixed reference trajectory. The proposed algorithm uses a model constructed by dynamic partial least squares (DyPLS) to extract the principal components of multiple input variables in each batch and designs the PTP-ILMPC controller in the latent variable space. Moreover, creating a reference trajectory updated along the batch direction through the desired points. The updating reference trajectory, modified according to the tracking error information, fully utilizes the degrees of freedom of nonspecific tracking points in the latent variable space. The proposed algorithm uses DyPLS to decouple the multiple-input, multiple-output system (MIMO) into a single-input, single-output system, simplifying the multivariable control problem. In addition, the updated reference relaxes the constraints on the system output so that the algorithm has a faster convergence speed and a more comprehensive range of applications. Two studies are proposed to show the effectiveness of the algorithm. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a latent variable point-to-point iterative learning model predictive control algorithm (LV-PTP-ILMPC) combined with the reference trajectory updating strategy is proposed. The algorithm uses a model constructed by dynamic partial least squares (DyPLS) to extract principal components and designs the control strategy in the latent variable space. The updated reference trajectory relaxes the constraints on the system output, leading to faster convergence and broader applicability of the algorithm.