# On Robustness in Optimization-Based Constrained Iterative Learning Control

arXiv: https://arxiv.org/abs/2203.05291 · HTML: https://mlsanity.com/paper/2203.05291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch](https://mlsanity.com/paper/2204.04722/) (2022)

## Abstract (arXiv)

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.