文献类型：期刊文献

英文题名：Model-free inversion-based iterative learning control algorithm with adaptive gain: Achieving superior robustness and convergence

作者：Kou, Zhicheng[1];Sun, Jinggao[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2024

卷号：45

期号：5

起止页码：2312

外文期刊名：OPTIMAL CONTROL APPLICATIONS & METHODS

收录：;EI(收录号：20242516286409);WOS:【SCI-EXPANDED(收录号:WOS:001250409900001)】；

基金：This work is supported by National Natural Science Foundation of China (62333005, 62073143).

语种：英文

外文关键词：gain design; iterative learning control; model-free inversion-based algorithm; robustness

摘要：The main objective of this work is to address the challenge of simultaneously ensuring robustness and convergence performance in model-free inversion-based iterative learning control. Initially, this research provides a mathematical analysis of the sources of errors in the iterative process, followed by proposing a gain design guideline to enhance both convergence speed and the final value error. Based on the gain design guideline, a gain design method associated with the number of iterations is proposed, resulting in a novel model-free inversion-based iterative learning control algorithm. Subsequently, a robustness analysis of the proposed algorithm is conducted. Finally, a comprehensive simulation and numerical comparison of the proposed algorithm with existing MFIIC-like algorithms are presented to demonstrate the superior performance of the proposed control algorithm. In this work, the sources of error in the iterative process are mathematically analysed, and a gain design guideline is proposed to improve the convergence speed and the final value error. Based on the gain design guideline, a gain design method related to the number of iterations is proposed, resulting in a novel model-free inversion-based iterative learning control algorithm. image