Consensus approximation and impulsive control for a class of uncertain multi-agent dynamics

TL;DR

This paper addresses consensus in uncertain multi-agent systems with time-varying interactions, proposing control strategies to achieve desired consensus despite uncertainties, validated through simulations.

Contribution

It introduces linear programming methods for bounding consensus values and controlling agent states under uncertain, time-varying interactions.

Findings

Established bounds on consensus values under uncertainty

Developed control strategies using linear programming

Validated approaches through numerical simulations

Abstract

This paper studies a class of consensus dynamics where the interactions between agents are affected by a time-varying unknown scaling factor. This situation is encountered in the control of robotic fleets over a wireless network or in opinion dynamics where the confidence given to the peers varies in time. Firstly, we establish conditions under which practical upper and lower bounds on the consensus value can be determined. Secondly, we propose control strategies for allocating a given control budget to shift agent states towards a desired consensus value despite the uncertainty. We provide computationally efficient linear programming-based approaches for both problems and validate the obtained results in numerical simulations.