Quadratic-Criterion-Based Model Predictive Iterative Learning Control for Batch Processes Using Just-in-Time-Learning Method

Liuming Zhou,Li Jia,Yu-Long Wang

doi:10.1109/access.2019.2934474

Abstract

In this paper, a new quadratic-criterion-based model predictive iterative learning control (QMPILC) algorithm for tracking problem of batch processes is proposed. In the proposed QMPILC design, a parametric time-varying model consisting of a set of local models is established for nonlinear batch processes by using the just-in-time-learning method. In order to describe the processes more accurately, the model is updated with batch running. On basis of the identification model, iterative learning control is combined with model predictive control based on a quadratic performance criterion, and the control law can be obtained by solving a convex optimization problem. According to the real-time feedback information, the input is updated to reject real-time disturbance. As a result, the proposed QMPILC algorithm improves control performance and optimization efficiency. In addition, the convergence and tracking performance of QMPILC are analyzed. The proposed methods are illustrated on batch reactor. The results are provided to show excellent performance of tracking product qualities.

Highlights

Iterative learning control (ILC) is an effective control technique for systems which have a repeat movement characteristic
ILC is suitable for the control systems whose control task is tracking desired trajectory and ends in a finite time, and it has been widely applied in batch processes whose characteristics are repetitive, nonlinear and time-varying [4], [5]
A model predictive iterative learning control algorithm based on quadratic-criterion for batch processes has been proposed in this paper

Summary

Introduction

Iterative learning control (ILC) is an effective control technique for systems which have a repeat movement characteristic. The general idea of ILC is to update control signal for the current batch by using the information of the pervious batches. Output trajectory converges to desired reference trajectory after several iterations [1]–[3]. ILC is suitable for the control systems whose control task is tracking desired trajectory and ends in a finite time, and it has been widely applied in batch processes whose characteristics are repetitive, nonlinear and time-varying [4], [5]. Batch processes systems are one of the most important research areas in process industry [6], and they have been widely applied to the manufacture of low-volume and high-value products such as semiconductors, pharmaceuticals, polymeric materials, and injection products. Batch processes systems are one of the most important research areas in process industry [6], and they have been widely applied to the manufacture of low-volume and high-value products such as semiconductors, pharmaceuticals, polymeric materials, and injection products. [7].

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