Data-Driven Cooperative Output Regulation via Distributed Internal Model

TL;DR

This paper extends data-driven cooperative output regulation methods to multi-input multi-output systems over general networks, simplifying computations and relaxing solvability conditions for unknown linear multi-agent systems.

Contribution

It introduces simplified linear systems for reinforcement learning and decouples equations, reducing computational complexity and easing solvability constraints.

Findings

Reduced computational cost due to simplified linear systems.

Decoupling of linear algebraic equations enhances solvability.

Applicable to general static and connected digraphs.

Abstract

The existing result on the cooperative output regulation problem for unknown linear multi-agent systems using a data-driven distributed internal model approach is limited to the case where each follower is a single-input and single-output system and the communication network among all agents is an acyclic static digraph. In this paper, we further address the same problem for unknown linear multi-agent systems with multi-input and multi-output followers over a general static and connected digraph. Further we make two main improvements over the existing result. First, we derive a set of much simplified linear systems to be applied by the integral reinforcement learning technique. Thus, the number of the unknown variables governed by a sequence of linear algebraic equations is much smaller than that of the existing approach. Second, we show that the sequence of linear algebraic equations can be further decoupled to two sequences of linear algebraic equations. As a result of these two improvements, our approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions in terms of the the full column rank requirements for these equations.