Control of Networked Multiagent Systems with Uncertain Graph Topologies

TL;DR

This paper introduces an adaptive control framework for multiagent systems that can handle uncertainties in network graph topologies, ensuring system trajectories stay close to desired reference behaviors despite topology variations.

Contribution

It proposes a novel adaptive controller architecture that compensates for uncertain and time-varying network topologies in multiagent systems.

Findings

Controller drives system trajectories close to reference model

Asymptotic convergence for constant uncertainties

Applicable to various networked multiagent problems

Abstract

Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.