Emerging robust and data‐driven control methods for uncertain learning systems.

Learning systems represent a particularly important class of practical data-driven systems that adapt to their environment based on the environment's response to the system's action. Despite the success of learning-based methods, finding suitable control frameworks for learning systems when there is uncertainty in the assumptions related to the system dynamics is still an open problem. They propose a novel constrained ILC design and further develop a decentralized implementation of the resulting ILC algorithm using the alternating direction method of multipliers, allowing the design to scale up to handle large-scale and varying system dynamics. [Extracted from the article]