Reinforcement Learning Based Distributed Control of Dissipative Networked Systems

We consider the problem of designing distributed controllers to stabilize a class of networked systems, where each subsystem is dissipative and designs a reinforcement learning based local controller to maximize an individual cumulative reward function. We develop an approach that enforces dissipativity conditions on these local controllers at each subsystem to guarantee stability of the entire networked system. The proposed approach is illustrated on a dc microgrid example, where the objective is to maintain voltage stability of the network using locally distributed controllers at each generation unit.