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

TL;DR

This paper introduces a fast, data-driven iterative learning control method for linear systems with unknown dynamics, disturbances, and input constraints, validated through experiments and a robotic case study.

Contribution

It proposes a non-parametric data-driven system representation and a novel accelerated ILC approach for robust control under uncertainties and constraints.

Findings

Effective disturbance rejection demonstrated

Accelerated convergence achieved

Validated on robotic motion system

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.