Distributed Robust Output Regulation of Heterogeneous Uncertain Linear Agents by Adaptive Internal Model Principle
This work addresses the output regulation problem for multi-agent systems with uncertain dynamics and time-varying topologies, providing a distributed solution that relaxes the need for all agents to access the exosystem.
The paper proposes a fully distributed controller for multi-agent output regulation, handling heterogeneous uncertain linear agents and time-varying directed networks, achieving perfect output regulation via an adaptive internal model principle. The approach generalizes the centralized internal model principle to distributed networks.
We study a multi-agent output regulation problem, where not all agents have access to the exosystem's dynamics. We propose a fully distributed controller that solves the problem for linear, heterogeneous, and uncertain agent dynamics as well as time-varying directed networks. The distributed controller consists of two parts: (1) an exosystem generator that locally estimates the exosystem dynamics, and (2) a dynamic compensator that, by locally approaching the internal model of the exosystem, achieves perfect output regulation. Moreover, we extend this distributed controller to solve an output synchronization problem where not all agents initially have the same internal model dynamics. Our approach leverages methods from internal model based controller synthesis and multi-agent consensus over time-varying directed networks; the derived result is a generalization of the (centralized) internal model principle to the distributed, networked setting.