  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.
