文献类型：期刊文献

英文题名：Test-based model-free adaptive iterative learning control with strong robustness

作者：Kou, Zhicheng[1];Sun, Jinggao[1,2]

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

年份：2023

卷号：54

期号：6

起止页码：1213

外文期刊名：INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

收录：;EI(收录号：20231313794522);WOS:【SCI-EXPANDED(收录号:WOS:000948609500001)】；

基金：This work was supported by National Natural Science Foundation of China [62073143].

语种：英文

外文关键词：Iterative learning control (ILC); nonlinear control; robustness; model-free control algorithm; model-free adaptive control

摘要：A test-based model-free adaptive iterative learning control algorithm (TB-MFAILC) with strong robustness is proposed in this paper. The algorithm improves the situation where existing model-free adaptive iterative learning control algorithms fail to converge or converge relatively slowly in noisy environments. Also, this work demonstrates the convergence and robustness of the proposed algorithm in different environments. Subsequently, the effectiveness of the proposed algorithm is illustrated by numerical comparison simulations with the existing model-free adaptive iterative learning control algorithm and the PD-based adaptive switching learning control algorithm in noisy environments. Finally, the advantages of the proposed algorithm are further illustrated through the analysis of relevant parameters.