Reinforcement Learning based Distributed Control of Dissipative Networked Systems

TL;DR

This paper proposes a reinforcement learning-based method for designing distributed controllers that ensure stability in dissipative networked systems, demonstrated through a voltage stability case in microgrids.

Contribution

It introduces a novel approach combining reinforcement learning with dissipativity constraints to guarantee stability in distributed control of networked systems.

Findings

Successfully stabilizes a microgrid example.

Ensures dissipativity conditions for local controllers.

Demonstrates practical effectiveness of the method.

Abstract

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 maintain voltage stability of the network using local distributed controllers at each generation unit.