Distributed event-driven adaptive three-dimensional formation tracking of networked autonomous underwater vehicles with unknown nonlinearities
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, J.H. | - |
dc.contributor.author | Yoo, S.J. | - |
dc.date.accessioned | 2021-07-23T05:48:41Z | - |
dc.date.available | 2021-07-23T05:48:41Z | - |
dc.date.issued | 2021-08-01 | - |
dc.identifier.issn | 0029-8018 | - |
dc.identifier.issn | 1873-5258 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47791 | - |
dc.description.abstract | This paper presents a distributed event-driven adaptive formation control strategy for networked uncertain nonlinear autonomous underwater vehicles (AUVs) in three-dimensional space. It is assumed that the leader information is only transmitted to a subset of AUV followers under a directed graph and the nonlinearities of the AUV dynamics are unknown. A distributed error transformation method is presented to address the distributed formation tracking problem of AUV followers in three-dimensional space. Then, a distributed event-driven adaptive control method using neural networks and stabilizing auxiliary signals is developed to ensure the stability of the event-driven closed-loop system and accomplish three-dimensional formation tracking in the Lyapunov stability sense. Additionally, it is shown that Zeno behavior does not occur in the resulting event-triggering strategy. A simulation demonstrates the effectiveness of the proposed theoretical methodology. © 2021 Elsevier Ltd | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Distributed event-driven adaptive three-dimensional formation tracking of networked autonomous underwater vehicles with unknown nonlinearities | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.oceaneng.2021.109069 | - |
dc.identifier.bibliographicCitation | Ocean Engineering, v.233 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000661134200021 | - |
dc.identifier.scopusid | 2-s2.0-85107052192 | - |
dc.citation.title | Ocean Engineering | - |
dc.citation.volume | 233 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Autonomous underwater vehicles (AUVs) | - |
dc.subject.keywordAuthor | Distributed formation | - |
dc.subject.keywordAuthor | Event-driven tracking | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Three-dimensional space | - |
dc.subject.keywordPlus | TRAJECTORY TRACKING | - |
dc.subject.keywordPlus | TARGET | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Oceanography | - |
dc.relation.journalWebOfScienceCategory | Engineering, Marine | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.relation.journalWebOfScienceCategory | Engineering, Ocean | - |
dc.relation.journalWebOfScienceCategory | Oceanography | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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