论文标题
基于增强学习的基于耗散网络系统的分布式控制
Reinforcement Learning based Distributed Control of Dissipative Networked Systems
论文作者
论文摘要
我们考虑设计分布式控制器以稳定一类网络系统的问题，在该系统中，每个子系统都是耗散的，并设计了基于强化学习的本地控制器，以最大程度地提高单个累积奖励功能。我们开发了一种方法，该方法可以在每个子系统的这些本地控制器上执行耗散条件，以确保整个网络系统的稳定性。在DC微电网示例中说明了所提出的方法，其中目标是使用每个生成单元的局部分布式控制器维持网络的电压稳定性。
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.
