Distributed Control

Distributed control focuses on coordinating the actions of multiple independent agents, such as robots or energy hubs, to achieve a common goal without relying on a central controller. Current research emphasizes robust solutions that address challenges like communication delays, self-localization errors, and the need for efficient collaboration in complex environments. Prominent approaches include model predictive control, reinforcement learning (both centralized and federated), and the use of graph neural networks to learn safe controllers. These advancements are crucial for enabling safe and efficient operation of increasingly complex systems in diverse applications, from multi-robot teams to decentralized energy grids and wireless communication networks.