Work

Predictive Optimal Iterative Learning Control for Nonlinear Systems Using the Koopman Operator

Year: 2025

Type: article

Abstract: ABSTRACT This paper develops a predictive optimal iterative learning control design for nonlinear systems based on the Koopman operator. Iterative learning control applies to systems that undergo repeated executions, known as trials, over a finite duration, the trial length. Once a trial is complete, all information generated is available to update the control signal for the subsequent trial. The first step in design is to approximately model the nonlinear system as a high‐dimensional linear model using the Koopman operator and extended dynamic mode decomposition, which is applied on each trial. Then, an iterative learning control law is designed with predictive action over an infinite duration in the trial‐to‐trial direction. The robust convergence of the tracking error is analyzed, and a numerical case study demonstrates the effectiveness of the design.

Source: International Journal of Adaptive Control and Signal Processing

DOI: 10.1002/acs.70008

Authors: Xinyue Tao, Hongfeng Tao, Luyuan Gao, Wojciech Paszke, Eric Rogers

Institutions: Jiangnan University, University of Zielona Góra, University of Southampton

Language: English

Topic: Iterative Learning Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Higher Education Discipline Innovation Project

Awards: G6625608275, G8104297890