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

Abstract

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.