Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7893
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTatlıcıoğlu, Evren-
dc.contributor.authorÇobanoğlu, Necati-
dc.contributor.authorZergeroğlu, Erkan-
dc.date.accessioned2020-07-18T03:35:20Z-
dc.date.available2020-07-18T03:35:20Z-
dc.date.issued2018-
dc.identifier.issn2475-1456-
dc.identifier.urihttps://doi.org/10.1109/LCSYS.2017.2720735-
dc.identifier.urihttps://hdl.handle.net/11147/7893-
dc.description.abstractIn this letter, position tracking control problem of a class of fully actuated Euler Lagrange (EL) systems is aimed. The reference position vector is considered to be periodic with a known period. Only position measurements are available for control design while velocity measurements are not. Furthermore, the dynamic model of the EL systems has parametric and/or unstructured uncertainties which avoid it to be used as part of the control design. To address these constraints, an output feedback neural network-based repetitive learning control strategy is preferred. Via the design of a dynamic model independent velocity observer, the lack of velocity measurements is addressed. To compensate for the lack of dynamic model knowledge, universal approximation property of neural networks is utilized where an online adaptive update rule is designed for the weight matrix. The functional reconstruction error is dealt with the design of a novel repetitive learning feedforward term. The outcome is a dynamic model independent output feedback neural network-based controller with a repetitive learning feedforward component. The stability of the closed-loop system is investigated via rigorous mathematical tools with which semi-global asymptotic stability is ensured. © 2017 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Control Systems Lettersen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLyapunov methodsen_US
dc.subjectNeural networksen_US
dc.subjectNonlinear output feedbacken_US
dc.titleNeural network-based repetitive learning control of euler lagrange systems: An output feedback approachen_US
dc.typeArticleen_US
dc.institutionauthorTatlıcıoğlu, Evren-
dc.institutionauthorÇobanoğlu, Necati-
dc.departmentIzmir Institute of Technology. Electronics and Communication Engineeringen_US
dc.identifier.volume2en_US
dc.identifier.issue1en_US
dc.identifier.startpage13en_US
dc.identifier.endpage18en_US
dc.identifier.wosWOS:000658895300003en_US
dc.identifier.scopus2-s2.0-85057640943en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/LCSYS.2017.2720735-
dc.relation.doi10.1109/LCSYS.2017.2720735en_US
dc.coverage.doi10.1109/LCSYS.2017.2720735en_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Oct 1, 2022

WEB OF SCIENCETM
Citations

7
checked on Oct 1, 2022

Page view(s)

54
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.