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Article: Leader-Follower Consensus Over Finite Fields

TitleLeader-Follower Consensus Over Finite Fields
Authors
KeywordsArtificial neural networks
characteristic polynomial
Finite element analysis
Finite-field networks
Galois fields
leader-follower consensus
Multi-agent systems
multi-agent systems
Synchronization
Task analysis
Time complexity
transition graph
Issue Date15-Jan-2024
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Automatic Control, 2024 How to Cite?
Abstract

In this paper, we investigate the leader-follower consensus of multi-agent systems over finite fields, which model agents with limited capacities for storing, processing, and transmitting the information, from the perspectives of the transition graph and the characteristic polynomial of the network matrix, respectively. By the features of dynamics over finite fields, we reveal that the transition graph of networks achieving the leader-follower consensus over finite fields is either a spanning in-tree topped at zero-state or is composed of spanning in-trees with the same structure, topped at steady states. To address the high time complexity associated with transition graphs and existing methods, we integrate the characteristic polynomial of the network matrix with the cycle and tree structures in the transition graph. As a result, a concise criterion is established only based on the characteristic polynomial, which merely requires polynomial computational complexity with respect to the number of network nodes. Finally, a numerical example is presented to validate the effectiveness of the obtained theoretical results.


Persistent Identifierhttp://hdl.handle.net/10722/341760
ISSN
2023 Impact Factor: 6.2
2023 SCImago Journal Rankings: 4.501

 

DC FieldValueLanguage
dc.contributor.authorLin, Lin-
dc.contributor.authorCao, Jinde-
dc.contributor.authorLam, James-
dc.contributor.authorZhu, Shiyong-
dc.contributor.authorAzuma, Shun-ichi-
dc.contributor.authorRutkowski, Leszek-
dc.date.accessioned2024-03-26T05:36:59Z-
dc.date.available2024-03-26T05:36:59Z-
dc.date.issued2024-01-15-
dc.identifier.citationIEEE Transactions on Automatic Control, 2024-
dc.identifier.issn0018-9286-
dc.identifier.urihttp://hdl.handle.net/10722/341760-
dc.description.abstract<p>In this paper, we investigate the leader-follower consensus of multi-agent systems over finite fields, which model agents with limited capacities for storing, processing, and transmitting the information, from the perspectives of the transition graph and the characteristic polynomial of the network matrix, respectively. By the features of dynamics over finite fields, we reveal that the transition graph of networks achieving the leader-follower consensus over finite fields is either a spanning in-tree topped at zero-state or is composed of spanning in-trees with the same structure, topped at steady states. To address the high time complexity associated with transition graphs and existing methods, we integrate the characteristic polynomial of the network matrix with the cycle and tree structures in the transition graph. As a result, a concise criterion is established only based on the characteristic polynomial, which merely requires polynomial computational complexity with respect to the number of network nodes. Finally, a numerical example is presented to validate the effectiveness of the obtained theoretical results.<br></p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Automatic Control-
dc.subjectArtificial neural networks-
dc.subjectcharacteristic polynomial-
dc.subjectFinite element analysis-
dc.subjectFinite-field networks-
dc.subjectGalois fields-
dc.subjectleader-follower consensus-
dc.subjectMulti-agent systems-
dc.subjectmulti-agent systems-
dc.subjectSynchronization-
dc.subjectTask analysis-
dc.subjectTime complexity-
dc.subjecttransition graph-
dc.titleLeader-Follower Consensus Over Finite Fields-
dc.typeArticle-
dc.identifier.doi10.1109/TAC.2024.3354195-
dc.identifier.scopuseid_2-s2.0-85182921817-
dc.identifier.eissn1558-2523-
dc.identifier.issnl0018-9286-

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