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.
@article{arxiv.2312.14326,
title = {Fast data-driven iterative learning control for linear system with output disturbance},
author = {Jia Wang and Leander Hemelhof and Ivan Markovsky and Panagiotis Patrinos},
journal= {arXiv preprint arXiv:2312.14326},
year = {2023}
}