Fast data-driven iterative learning control for linear system with output disturbance

2023/12/21 by Jia Wang, Wang, Jia, Leander Hemelhof +5
Engineering · #FOS: Electrical engineering #Iterative Learning Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2312.14326

openalex publication_date 2023/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions are: 1) using a non-parametric data-driven representation of the system dynamics, for dealing with the unknown system dynamics in the context of ILC, 2) design of a fast ILC method for dealing with output disturbances, model uncertainty and input constraints. A complete design method is given in this paper, which consists of the data-driven representation, controller formulation, acceleration strategy and convergence analysis. A batch of numerical experiments and a case study on a high-precision robotic motion system are given in the end to show the effectiveness of the proposed method.

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