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On the tuning of the lagrangian parameters in a uniform structure in adaptative robot control

dc.contributor.authorRudas, Imre J.
dc.contributor.authorTenreiro Machado, J. A.
dc.contributor.authorKaynak, Okyay M.
dc.date.accessioned2019-04-12T15:05:20Z
dc.date.available2019-04-12T15:05:20Z
dc.date.issued1998-08-04
dc.description.abstractIn this paper the application of "uniform structures" formerly introduced for the Hamiltonian description of robtos is investigated in the case of a SCARA within the frames of the Lagrangian model. This model does not suffer from measurability problems of the Hamiltonian one. Via simulation it was found that this simple method can improve the control of an imperfectly modeled system under unmodeled environmental interaction. It uses the Lie parameters of the Orthogonal Group and the "Sliding Simplex Algorithm" for the on-line tuning the parameters in the uniform structures. This method is very similar to the learning process of the artificial neural Networks. To evade the problem of local optima tuning starts from the estimtaed vicinity of the Global Optimum at the begining. It is shown that a fast enough tuning can "stick" in this optimum and it propagates together with it in time as the local dynamics of the system is changes in time.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/13562
dc.language.isoengpt_PT
dc.titleOn the tuning of the lagrangian parameters in a uniform structure in adaptative robot controlpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceGold Coast, Australiapt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2nd IEEE International Conference on Intelligent Processing 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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