Random Learning Leads to Faster Convergence in ‘Model-Free’ ILC

With Application to MIMO Feedforward in Industrial Printing

Journal article (2024)

Authors

Leontine Aarnoudse Eindhoven University of Technology
Tom Oomen Eindhoven University of Technology, Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2024

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Model-free iterative learning control (ILC) can lead to high performance by attenuating repeating disturbances completely, using dedicated experiments on the real system to replace the traditional model. The aim of this paper is to develop a fast data-driven method for MIMO ILC that uses random learning in the form of efficient unbiased gradient estimates. This is achieved by developing a stochastic conjugate gradient algorithm, in which the search direction and optimal step size are generated using dedicated experiments. The approach is applied to MIMO automated feedforward tuning. Simulation and experimental results show that the method is superior to earlier stochastic and deterministic methods.