An integrated tube robust iterative learning model predictive control strategy based on dynamic partial least squares algorithm for batch processes

被引：0

作者：

Zhou, Liuming ^{[1
,3
]}

Zheng, Chuangkai ^{[1
]}

Li, Feng ^{[2
]}

机构：

[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China

[2] Jiangsu Univ Technol, Changzhou, Peoples R China

[3] Yangzhou Univ, Yangzhou 225100, Peoples R China

来源：

CANADIAN JOURNAL OF CHEMICAL ENGINEERING | 2024年 / 102卷 / 09期

基金：

中国国家自然科学基金;

关键词：

batch processes; dynamic partial least squares; latent variable iterative learning model predictive control; model parameter uncertainty; tube-based model predictive control; COVALENT ORGANIC FRAMEWORKS; SOLID-ELECTROLYTE INTERPHASE; ANODE MATERIALS; METAL ANODES; LITHIUM; ENERGY; BATTERY; CONDUCTION; DENDRITE; GROWTH;

D O I：

10.1002/cjce.25261

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

In this paper, an integrated tube robust iterative learning model predictive control (Tube-RILMPC) strategy based on the dynamic partial least squares (DyPLS) identified algorithm is proposed. The problems of large amount of online data calculation and input and output variable dimensions disaster in the original variable space are solved. This integrated Tube-RILMPC strategy enhances the tracking performance and robustness of two-dimensional (2D) control system. The output trajectories of system are located in the tube the nominal trajectory, which reduces the modelling error caused by the uncertain model. Based on the worst-case performance index of ellipsoidal uncertainty and polytopic uncertainty, a robust iterative learning control (ILC) strategy is designed. Finally, the superiority of the proposed control algorithm is verified by comparative simulation.

引用

页码：3213 / 3235

页数：23

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