Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

Libin Xu; Weimin Zhong; Jingyi Lu; Furong Gao; Feng Qian; Zhixing Cao

doi:10.1021/acsomega.2c01741

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

ACS Omega. 2022 May 30;7(23):19939-19947. doi: 10.1021/acsomega.2c01741. eCollection 2022 Jun 14.

Authors

Libin Xu¹, Weimin Zhong¹, Jingyi Lu^{1

2}, Furong Gao³, Feng Qian¹, Zhixing Cao¹

Affiliations

¹ MOE Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai 200237, China.
² Department of Electrical Engineering and Information Technology, Paderborn University, 33098, Paderborn, Germany.
³ Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Abstract

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.