Decentralized Control

Decentralized control focuses on coordinating multiple agents or robots without a central authority, aiming for robustness, scalability, and efficiency in complex tasks. Current research emphasizes the development and application of algorithms like model predictive control, deep reinforcement learning (particularly DQN), and graph neural networks, often incorporating control barrier functions for safety guarantees. This field is crucial for advancing multi-robot systems in diverse applications, from swarm robotics and autonomous driving to collaborative manipulation and aerial transportation, by enabling flexible and fault-tolerant coordination in dynamic environments.