Skip to main navigation Skip to search Skip to main content

Group Consensus for Heterogeneous Multiagent Systems in the Competition Networks with Input Time Delays

Lianghao Ji, Xinghuo Yu, Chaojie Li*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

The group consensus problems of heterogeneous multiagent networks with input time delays are investigated in this paper. In this complex networks, the agents' dynamics are modeled by the first-order and the second-order multiagent systems, where a novel dynamic group consensus protocol is designed through the competitive relationship among the agents. By using matrix theory and the frequency-domain method, some algebraic criteria are theoretically established for reaching a group consensus in the following two cases: 1) with the identical and 2) different input time delays. Meanwhile, the conservative assumptions existed in the relevant literatures are absolutely relaxed, i.e., the in-degree balance and the geometric multiplicity of zero eigenvalue of Laplacian matrix are at least two. Finally, the effectiveness of our results is illustrated by numerical examples. © 2018 IEEE.
Original languageEnglish
Pages (from-to)4655-4663
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume50
Issue number11
Online published9 Aug 2018
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61572091, Grant 61673080, and Grant 61876200, and in part by the Natural Science Foundation Project of the Chongqing Science and Technology Commission under Grant cstc2018jcyjAX0112.

Research Keywords

  • Complex networks
  • consensus
  • group consensus
  • heterogeneous multiagent systems
  • time delay

Fingerprint

Dive into the research topics of 'Group Consensus for Heterogeneous Multiagent Systems in the Competition Networks with Input Time Delays'. Together they form a unique fingerprint.

Cite this