文献类型：期刊文献

英文题名：Optimally Selected Cycle-Based ILC for System With Randomly Varying Initial State

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

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

年份：2025

卷号：70

期号：4

起止页码：2714

外文期刊名：IEEE TRANSACTIONS ON AUTOMATIC CONTROL

收录：;EI(收录号：20244717394818);WOS:【SCI-EXPANDED(收录号:WOS:001455456900033)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62394343, in part by the Hong Kong Research Grant Council under Grant 16203322 and Grant N_HKUST628/22, in part by the National Science Foundation of China under Grant 62403359, and in part by the Fundamental Research Funds for the Central Universities, China under Grant 22120240010.

语种：英文

外文关键词：Learning systems; Uncertain systems; Trajectory; Convergence; Sensitivity; Optimization; Manipulators; Indexes; Chemicals; Biology; Iterative learning control (ILC); optimization; randomly varying initial state; repetitive process

摘要：Iterative learning control (ILC) is a widely used method for controlling repetitive processes. However, its superior learning capability from cycle to cycle is mostly predicated on the assumption that the initial state for all cycles is identical and at the desired point. In engineering practice, this assumption can be overly strict. A more common scenario involves the initial state varying randomly from cycle to cycle. In this article, we propose an optimally selected cycle-based ILC scheme to address the issue of randomly varying initial states. Our approach involves selecting an optimal cycle for iterative learning by evaluating both the potential impact of initial state variations and the tracking performance of historical cycles. By extending the learning mechanism of ILC from learning from the previous cycle to learning from the past optimally selected cycle, our scheme ensures improvement after each iteration of learning. In addition, our scheme has been adapted to accommodate uncertain systems with greater generality. The feasibility and convergence properties of our scheme are investigated through theoretical analysis. Finally, we demonstrate the effectiveness and other properties of the proposed method through a benchmark numerical example.