文献类型：会议论文

英文题名：A Virtual Cycle -based Iterative learning Control Framework for Repetitive System with Randomly Varying Initial State

作者：Gao, Kaihua[1];Zhou, Yuanqiang[2,3];Lu, Jingyi[3];Cao, Zhixing[4];Gao, Furong[1,5]

机构：[1]Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Kowloon, Hong Kong, Peoples R China;[2]Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China;[3]East China Univ Sci & Technol, MOE Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[4]Queens Univ, Dept Chem Engn, Kingston, ON, Canada;[5]Guangzhou HKUST Fok Ying Tung Res Inst, Guangzhou 511458, Peoples R China

会议论文集：12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

会议日期：JUL 14-17, 2024

会议地点：Toronto, CANADA

语种：英文

外文关键词：Iterative learning control; Repetitive process; Process control; Optimization; Chemical engineering

摘要：Iterative learning control (ILC) has been considered a powerful strategy for repetitive process control. However, a fundamental assumption of conventional ILC is that each cycle must start from a predetermined fixed initial state. This assumption can be strict and challenging to achieve in real-world industrial applications. To address the issues arising from varying initial states, we propose an ILC framework that learns from a virtual cycle generated using historical data. We establish three conditions for generating the virtual cycle, and theoretical results demonstrate guaranteed convergence. To ensure the practicality of our framework, we relax one of the conditions, enabling the virtual cycle to be generated by solving a convex optimization problem. The effectiveness of our framework in improving control performance is verified through an injection molding example.