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Stability margin of undirected homogeneous relative sensing networks: A geometric perspective

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dc.contributor.authorHamdipoor, Vahid-
dc.contributor.authorMoon, Jun-
dc.contributor.authorKim, Yoonsoo-
dc.date.accessioned2022-07-06T12:09:17Z-
dc.date.available2022-07-06T12:09:17Z-
dc.date.created2021-11-22-
dc.date.issued2021-10-
dc.identifier.issn0167-6911-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140901-
dc.description.abstractIn this paper, we study the stability margin (SM) of undirected homogeneous relative sensing networks (UH-RSNs) from a geometric point of view. SM is an important robustness measure indicating the amount of simultaneous gain and phase perturbations in the feedback channels before the instability occurs. A UH-RSN is characterized by the identical local dynamics (a single-input-single-output (SISO) open-loop transfer function T-loc(s)) of individual agent and the graph Laplacian L-g representing how the agents are connected. It is shown in this paper that UH-RSNs having multiple inputs and outputs in general may be represented as a unity feedback system including the SISO T-loc(s) and one of the real eigenvalues of L-g. This representation then helps to identify a class of cooperative T-loc(s) for which (1) SM becomes maximized or equal to 1 when the network's connectivity (the second smallest eigenvalue of L-g is greater than or equal to the curvature of the Nyquist plot of T-loc(s) at the origin; and (2) two bounds on SM are obtained for the SM estimation based on the geometric shape of Nyquist plot. Also, the representation of unity feedback system implies that UH-RSNs with non-cooperative T-loc(s) become unstable when the agents are joined with high connectivity. Numerical examples are provided to demonstrate these findings.-
dc.language영어-
dc.language.isoen-
dc.publisherELSEVIER-
dc.titleStability margin of undirected homogeneous relative sensing networks: A geometric perspective-
dc.typeArticle-
dc.contributor.affiliatedAuthorMoon, Jun-
dc.identifier.doi10.1016/j.sysconle.2021.105027-
dc.identifier.scopusid2-s2.0-85114128041-
dc.identifier.wosid000697711900005-
dc.identifier.bibliographicCitationSYSTEMS & CONTROL LETTERS, v.156, pp.1 - 7-
dc.relation.isPartOfSYSTEMS & CONTROL LETTERS-
dc.citation.titleSYSTEMS & CONTROL LETTERS-
dc.citation.volume156-
dc.citation.startPage1-
dc.citation.endPage7-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAutomation & Control Systems-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryAutomation & Control Systems-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.subject.keywordPlusMULTIAGENT SYSTEMS-
dc.subject.keywordPlusDELAY MARGIN-
dc.subject.keywordPlusCONSENSUS-
dc.subject.keywordPlusFEEDBACK-
dc.subject.keywordPlusSYNCHRONIZATION-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordAuthorStability margin-
dc.subject.keywordAuthorRelative sensing network-
dc.subject.keywordAuthorLaplacian matrix-
dc.subject.keywordAuthorNyquist plot-
dc.subject.keywordAuthorCurvature-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0167691121001572?via%3Dihub-
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