文献类型：期刊文献

英文题名：Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

作者：Xu, Libin[1];Zhong, Weimin[1];Lu, Jingyi[1,2];Gao, Furong[3];Qian, Feng[1];Cao, Zhixing[1]

机构：[1]East China Univ Sci & Technol, MOE Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Paderborn Univ, Dept Elect Engn & Informat Technol, D-33098 Paderborn, Germany;[3]Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Clear Water Bay, Hong Kong, Peoples R China

年份：2022

卷号：7

期号：23

起止页码：19939

外文期刊名：ACS OMEGA

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000811935600001)】；

基金：The work was supported by the National Natural Science Foundation of China under Grants 61925305, 62073137, 61988101, and Shanghai Action Plan for Technological Innovation Grant 22ZR1415300. The work is partially published as a conference paper in the Chinese Process Control Conference 2021.

语种：英文

摘要：Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.