Finite-time Consensus for Multi-agent Networks with Unknown Inherent Nonlinear Dynamics

TL;DR

This paper introduces a novel stability tool to analyze finite-time convergence of a nonlinear consensus algorithm in multi-agent networks with unknown nonlinear dynamics, ensuring convergence under directed switching graphs.

Contribution

The paper proposes a new stability analysis method based on a generalized comparison lemma for finite-time consensus in nonlinear multi-agent systems with unknown dynamics.

Findings

Finite-time convergence guaranteed under directed spanning trees.

A novel stability tool applicable to linear and nonlinear systems.

Simulation confirms theoretical results.

Abstract

This paper focuses on analyzing the finite-time convergence of a nonlinear consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis of the closed-loop system under the proposed consensus algorithm are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel stability tool based on a generalized comparison lemma. With the aid of the novel stability tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching interaction graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching interaction graph can even be guaranteed by the stability and the finite-time convergence of some special well-designed nonlinear closed-loop system under some special directed switching interaction graph, where each agent has at most one neighbor whose state is either the maximum of those states that are smaller than its own state or the minimum of those states that are larger than its own state. This provides a stimulating example for the potential applications of the proposed novel stability tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems. For illustration of the theoretical result, we provide a simulation example.