Distributed Consensus of Stochastic Multi-agent Systems with Prescribed Performance Constraints

TL;DR

This paper develops distributed control strategies for stochastic multi-agent systems that ensure consensus and meet predefined performance constraints despite stochastic noise in agent dynamics.

Contribution

It introduces sufficient conditions for achieving consensus in expectation and almost surely, considering state-dependent noise and prescribed performance constraints.

Findings

Ensures consensus in expectation and almost surely.

Guarantees prescribed performance constraints over system evolution.

Validated effectiveness through numerical example.

Abstract

This paper focuses on the problem of distributed consensus control of multi-agent systems while considering two main practical concerns (i) stochastic noise in the agent dynamics and (ii) predefined performance constraints over evolutions of multi-agent systems. In particular, we consider that each agent is driven by a stochastic differential equation with state-dependent noise which makes the considered problem more challenging compared to non-stochastic agents. The work provides sufficient conditions under which the proposed timevarying distributed control laws ensure consensus in expectation and almost sure consensus of stochastic multi-agent systems while satisfying prescribed performance constraints over evolutions of the systems in the sense of the qth moment. Finally, we demonstrate the effectiveness of the proposed results with a numerical example.