Research Article
Open-Closed-Loop Iterative Learning Control Based on Differential Evolution Algorithm for Nonlinear System
@INPROCEEDINGS{10.1007/978-3-031-73699-5_7, author={Mengtao Lei and Yun-Shan Wei and Sixian Xiong}, title={Open-Closed-Loop Iterative Learning Control Based on Differential Evolution Algorithm for Nonlinear System}, proceedings={Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25--26, 2023, Proceedings}, proceedings_a={SPNCE}, year={2025}, month={1}, keywords={Open-closed-loop differential evolution algorithm nonlinear discrete-time system iterative learning control}, doi={10.1007/978-3-031-73699-5_7} }- Mengtao Lei
Yun-Shan Wei
Sixian Xiong
Year: 2025
Open-Closed-Loop Iterative Learning Control Based on Differential Evolution Algorithm for Nonlinear System
SPNCE
Springer
DOI: 10.1007/978-3-031-73699-5_7
Abstract
This paper proposes an iterative learning control problem based on the differential evolution algorithm for optimal control gains. The proposed framework for a nonlinear discrete-time system consists of open-loop ILC component and closed-loop control component, forming an open-closed-loop ILC structure. The inclusion of the open-loop component guarantees the convergence of the ILC tracking error in terms of mathematical expectation. Feedback control accelerates convergence with appropriate gain. The control gain is optimized by the differential evolution algorithm to achieve better system control and faster convergence. After conducting ILC convergence analysis and simulation, the tracking error tends to approach zero in mathematical expectation.
