Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers

TL;DR

This paper introduces an adaptive control method for multi-agent systems that achieves cooperative output regulation without prior knowledge of exosystem dynamics, using adaptive dynamic programming and data-driven observers.

Contribution

It develops a novel adaptive optimal control approach that does not require exosystem knowledge, enabling data-driven learning of optimal policies in multi-agent systems.

Findings

Estimation errors and tracking errors converge to zero.

The method effectively achieves cooperative output regulation.

Simulation results validate the approach's efficacy.

Abstract

In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The proposed method is different from those in the existing literature of ADP and cooperative output regulation in the sense that the knowledge of the exosystem dynamics is not required in the design of the exostate observers for those agents with no direct access to the exosystem. Moreover, an optimal control policy is obtained without the prior knowledge of the modeling information of any agent while achieving the cooperative output regulation. Instead, we use the state/input information along the trajectories of the underlying dynamical systems and the estimated exostates to learn the optimal control policy. Simulation results show the efficacy of the proposed algorithm, where both estimation errors of exosystem matrix and exostates, and the tracking errors converge to zero in an optimal sense, which solves the cooperative optimal output regulation problem.