Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31529
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dc.contributor.authorDai, J-
dc.contributor.authorTan, P-
dc.contributor.authorXiao, L-
dc.contributor.authorWang, Z-
dc.contributor.authorHe, Y-
dc.contributor.authorZuo, Q-
dc.date.accessioned2025-07-10T13:16:48Z-
dc.date.available2025-07-10T13:16:48Z-
dc.date.issued2025-05-02-
dc.identifierORCiD: Lin Xiao https://orcid.org/0000-0003-3172-3490-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifier.citationDai, J. et al. (2025) 'Noise-tolerant fixed-time leader-follower consensus controller design for multi-agent systems via fuzzy-neural-network', Neural Computing and Applications, 0 (ahead of print), pp. 1 - 23. doi: 10.1007/s00521-025-11241-2.en_US
dc.identifier.issn0941-0643-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31529-
dc.description.abstractThe leader-follower consensus control problem in multi-agent systems (MASs) is critical and has received significant attention. However, the simultaneous achievement of fixed-time stability and robustness is often challenging in MASs due to their inherent complexity and uncertainty. This paper developed a controller based on the proposed noise-tolerant fixed-time fuzzy neural network (NF-FNN) model to realize the leader-follower consensus of MASs. Specifically, the introduction of an integral error term makes the NF-FNN model have powerful noise tolerance, and a fuzzy gain parameter generated by the Takagi-Sugeno fuzzy logic system makes the NF-FNN model have fuzzy adaptiveness. In addition, a new partition-sign-by-power activation function is developed to ensure fixed-time stability of the NF-FNN model. Theoretical analysis and comparative simulations confirm the superb swift stability and excellent noise tolerance of controllers based on the NF-FNN model for achieving the leader-follower consensus of MASs, as compared with existing methods.en_US
dc.description.sponsorshipThis work was supported in part by the Natural Science Foundation of Hunan Province of China under Grant 2022RC1103 and Grant 2024JJ6320, and in part by the National Natural Science Youth Foundation of China under Grant 62406109.en_US
dc.format.extent1 - 23-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.rightsCopyright The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00521-025-11241-2 (see: https://www.springernature.com/gp/open-research/policies/journal-policies).-
dc.rights.urihttps://www.springernature.com/gp/open-research/policies/journal-policies-
dc.subjectfuzzy neural networksen_US
dc.subjectTakagi-Sugeno fuzzy logic systemen_US
dc.subjectmulti-agent systemsen_US
dc.subjectleader-follower consensusen_US
dc.subjectnoise toleranceen_US
dc.titleNoise-tolerant fixed-time leader-follower consensus controller design for multi-agent systems via fuzzy-neural-networken_US
dc.typeArticleen_US
dc.date.dateAccepted2025-04-01-
dc.identifier.doihttps://doi.org/10.1007/s00521-025-11241-2-
dc.relation.isPartOfNeural Computing and Applications-
pubs.issueahead of print-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn1433-3058-
dcterms.dateAccepted2025-04-01-
dc.rights.holderThe Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature-
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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