Decentralized iterative learning control for constrained collaborative tracking

High performance collaborative tracking where multiple subsystems work together repetitively to track a reference has found applications in various areas. To meet the high accuracy requirements, iterative learning control (ILC) has recently been used. However, most existing designs ignore the system constraints that are often encountered in practice. Furthermore, they usually have a centralized structure (or controller parameter) that needs to be redesigned or re-tuned when the system changes which may cause difficulties for handling large scale systems. In this article, we consider the constrained collaborative tracking problem and propose a novel constrained ILC design. The resulting ILC algorithm guarantees not only the satisfaction of the system constraints, but also the monotonic convergence of the collaborative tracking error norm to a minimum (possible) value. We further develop a decentralized implementation of the proposed algorithm using the alternating direction method of multipliers, allowing the design to scale up to large scale and varying system dynamics. Finally, we provide numerical examples to verify the algorithms' effectiveness.