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Analysis and control of a dragonfly-inspired robot

dc.contributor.authorCouceiro, Micael S.
dc.contributor.authorFerreira, N. M. Fonseca
dc.contributor.authorTenreiro Machado, J. A.
dc.date.accessioned2019-04-18T10:52:17Z
dc.date.available2019-04-18T10:52:17Z
dc.date.issued2009
dc.description.abstractDragonflies demonstrate unique and superior flight performances than most of the other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper it is studied the dynamics of a dragonfly-inspired robot. The system performance is analyzed in terms of time response and robustness. The development of computational simulation based on the dynamics of the robotic dragonfly allows the test of different control algorithms. We study different movement, the dynamics and the level of dexterity in wing motion of the dragonfly. The results are positive for the construction of flying platforms that effectively mimic the kinematics and dynamics of dragonflies and potentially exhibit superior flight performance than existing flying platforms.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/13647
dc.language.isoengpt_PT
dc.subjectAerodynamicspt_PT
dc.subjectDynamicpt_PT
dc.subjectControlpt_PT
dc.subjectBirdpt_PT
dc.subjectRoboticpt_PT
dc.titleAnalysis and control of a dragonfly-inspired robotpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceLisbon 09pt_PT
oaire.citation.endPage9pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSymposium on Fractional Signals and Systemspt_PT
person.familyNameTenreiro Machado
person.givenNameJ. A.
person.identifier.ciencia-id7A18-4935-5B29
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id55989030100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscovery82cd5c17-07b6-492b-b3e3-ecebdad1254f

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