Coordination and control for large distributed sensor networks

As the complexity of power plants increase, so does the difficulty in accurately modeling the interactions among the subsystems. Distributed sensing and control offers a possible solution to this problem, but introduces a new one: how to ensure that each subsystem satisfying its control objective leads to the safe and reliable operation of the entire power plant. In this work we present a distributed coordination algorithm that offers safe, reliable, and scalable control of a distributed system. In this approach, each system component uses a reinforcement learning algorithms to achieve its own objectives, but those objectives are derived to coordinate implicitly and achieve the system level objective. We show that in a Time-Extended Defect Combination Problem where the agents need to determine when and whether or not they should be sensing in order to maintain QoS in a system, the proposed method outperforms traditional methods by up to two orders of magnitude.