Gao Weinan, Mynuddin Mohammed, Wunsch Donald C, Jiang Zhong-Ping
IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5229-5240. doi: 10.1109/TNNLS.2021.3069728. Epub 2022 Oct 5.
In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.
本文提出了一种数据驱动的分布式控制方法，以解决领导者-跟随者多智能体系统的协同最优输出调节问题。与传统的协同输出调节研究不同，本文首次开发了一种分布式自适应内模，它包括一个分布式内模和一个用于估计领导者动态的分布式观测器。在不依赖多智能体系统动态的情况下，我们提出了两种强化学习算法，即策略迭代和值迭代，通过在线输入和状态数据以及领导者状态的估计值来学习最优控制器。通过结合这些方法，我们为将数据分布式控制方法与自适应动态规划方法在总体上进行连接奠定了基础，因为这些是构建它们的理论基础。
