Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

TL;DR

This paper introduces a distributed Hankel matrix-based algorithm for high-order multi-agent systems that achieves minimal-time deadbeat consensus and predicts individual disagreement degrees in advance, outperforming existing asymptotic methods.

Contribution

It presents a novel fully distributed method for deadbeat consensus prediction and disagreement degree estimation in high-order MASs, with guaranteed minimal convergence time.

Findings

The proposed algorithm attains deadbeat consensus in minimal time.

It accurately predicts individual disagreement degrees beforehand.

Numerical simulations confirm the effectiveness and superiority of the method.

Abstract

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations.