Distributed Minimum-Time Consensus Prediction for High-Order Multiagent Systems

Abstract

In this note, a detective matrix-based distributed algorithm is proposed to address a distributed minimum-time consensus prediction problem for discrete-time high-order multiagent systems (MASs). Therein, each agent predicts the consensus value merely using the minimum length of individual and/or neighboring successive position time series. Compared to most existing algorithms that could only predict constant consensus values for first-order linear MASs, the present method could predict consensus manifolds for high-order linear MASs as well, which could be time-varying. Finally, both the effectiveness and superiority of the proposed distributed minimum-time consensus algorithm are substantiated by numerical simulations.