Robust Model Predictive Iterative Learning Control With Iteration-varying Reference Trajectory

被引：0

作者：

Ma L.-L. ^{[1
]}

Liu X.-J. ^{[1
]}

机构：

[1] School of Control And Computer Engineering, North China Electric Power University, Beijing

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 10期

基金：

中国国家自然科学基金;

关键词：

H[!sub]∞[!/sub] control; Iteration-varying reference trajectory; Iterative learning control (ILC); LPV model; Robust model predictive control;

D O I：

10.16383/j.aas.c180681

中图分类号：

学科分类号：

摘要：

Model predictive iterative learning control (MPILC) is a popular approach to control systems with repetitive nature like batch systems, as it is capable of tracking the plant reference trajectory with high accuracy and guaranteed closed-loop stability. However, the existing MPILCs are mostly based on linear/linearized system with no consideration of reference trajectory variation. In this paper, a robust MPILC (RMPILC) based on the linear parameter varying (LPV) model is derived to track the iteration-varying reference trajectory. The LPV model is chosen to represent the dynamic property of nonlinear systems accurately. Robust H∞ control is incorporated with MPILC to restrain the fluctuation of tracking errors, with control inputs solved by optimizing the objective function constrained by linear matrix inequalities. The robust stability and convergence condition of the system controlled by RMPILC are analyzed. The effectiveness of the proposed controller is verified through the simulations on a numerical example and a continuous stirred tank reactor (CSTR) system. Copyright © 2019 Acta Automatica Sinica. All rights reserved.

引用

页码：1933 / 1945

页数：12

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