On Robustness in Optimization-Based Constrained Iterative Learning Control

Iterative learning control (ILC) is a strategy for repetitive tasks wherein information from previous runs leveraged to improve future performance. Optimization-based ILC (OB-ILC) powerful design framework constrained where measurements the process are integrated into an optimization algorithm provide robustness against noise and modelling error. This paper proposes robust controller linear processes based on forward-backward splitting algorithm. It demonstrates how structured uncertainty can be ensure constraint satisfaction provides rigorous stability analysis in iteration domain by combining concepts monotone operator theory control. Numerical simulations of precision motion stage support theoretical results.