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arXiv · 2203.05291

On Robustness in Optimization-Based Constrained Iterative Learning Control

Dominic Liao-McPherson·Efe C. Balta·Alisa Rupenyan·John Lygeros
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Abstract

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.

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Dominic Liao-McPherson, Efe C. Balta, Alisa Rupenyan, John Lygeros. 2022-03-10. On Robustness in Optimization-Based Constrained Iterative Learning Control. https://arxiv.org/abs/2203.05291

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