Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based ILC (OB-ILC) is a powerful design framework for constrained ILC where measurements from the process are integrated into an optimization algorithm to provide robustness against noise and modelling error. This paper proposes a robust ILC controller for constrained linear processes based on the forward-backward splitting algorithm. It demonstrates how structured uncertainty information can be leveraged to ensure constraint satisfaction and provides a rigorous stability analysis in the iteration domain by combining concepts from monotone operator theory and robust control. Numerical simulations of a precision motion stage support the theoretical results.
@article{arxiv.2203.05291,
title = {On Robustness in Optimization-Based Constrained Iterative Learning Control},
author = {Dominic Liao-McPherson and Efe C. Balta and Alisa Rupenyan and John Lygeros},
journal= {arXiv preprint arXiv:2203.05291},
year = {2022}
}