Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model

Weinan Gao, Mohammed Mynuddin, Donald C. Wunsch, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalIEEE transactions on neural networks and learning systems
DOIs
StateAccepted/In press - 2021

Keywords

  • Adaptation models
  • Adaptive optimal control
  • cooperative output regulation
  • distributed adaptive internal model
  • Multi-agent systems
  • Optimal control
  • Power system dynamics
  • Regulation
  • reinforcement learning.
  • Symmetric matrices
  • Vehicle dynamics

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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