文献类型：期刊文献

英文题名：Hybrid-based model-free iterative learning control with optimal performance

作者：Kou, Zhicheng[1];Sun, Jinggao[1];Su, Guanghao[1];Wang, Meng[1];Yan, Huaicheng[1]

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

年份：2023

卷号：54

期号：10

起止页码：2268

外文期刊名：INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

收录：;EI(收录号：20232614297419);WOS:【SCI-EXPANDED(收录号:WOS:001012008900001)】；

基金：This work was supported by National Natural Science Foundation of China [62073143]~and~[62003139].

语种：英文

外文关键词：Iterative learning control (ILC); inversion-based control; model-free control; hybrid-based strategy

摘要：In this paper, a hybrid-based model-free iterative learning control algorithm is proposed to improve the robustness and convergence speed of model-free iterative learning control in noisy environments. The proposed algorithm divides the iterative process into a rapidly decreasing error phase and an error convergence phase, and uses different control algorithms in different phases, thus combining different advantages of the original algorithms. In addition to this, this work proves the convergence and robustness of the proposed algorithm and summarises the design idea of this controller. Finally, the convergence performance of the algorithm in noisy environments and in variable reference trajectory environment is simulated to demonstrate the effectiveness of the algorithm proposed in this work.