Neural Network Control of Distributed Cooperative Formation of Multi-agent System

Si Kheang Moeurn*, Bin Xin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The paper presented in this article deals with the issue of distributed cooperative formation of multi-agent systems (MASs). It proposes the use of appropriate neural network control methods to address formation requirements. The paper considers distributed cooperative formation control using a leader-follower approach. The paper also employs neural networks to overcome control challenges while dealing with complex systems or complex conditions. The neural network model was designed and the leader-follower formation control protocol was proposed. The sufficient conditions for the system stability were derived using Lyapunov stability theory, graph theory, and state space methods. By simulating the results of this study, the main data of the formation process can be observed to analyze and verify whether the system meets the requirements. Finally, by using an example of 16 agents to generate a hexagonal formation, it is verified that the system achieves consistency, stability, reliability, and accuracy in the cooperative formation.

Original languageEnglish
Title of host publicationAdvanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings
EditorsBin Xin, Naoyuki Kubota, Kewei Chen, Fangyan Dong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages283-292
Number of pages10
ISBN (Print)9789819975921
DOIs
Publication statusPublished - 2024
Event8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023 - Beijing, China
Duration: 3 Nov 20235 Nov 2023

Publication series

NameCommunications in Computer and Information Science
Volume1932 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023
Country/TerritoryChina
CityBeijing
Period3/11/235/11/23

Keywords

  • Formation Control
  • Multi-Agent Systems
  • Neural Network

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