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dc.contributor.authorAzar, Ahmad T.
dc.contributor.authorSerrano, Fernando E.
dc.contributor.authorRossell Garriga, Josep Maria
dc.contributor.authorVaidyanathan, Sundarapandian
dc.contributor.authorZhu, Quanmin
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2020-11-04T15:32:24Z
dc.date.available2021-11-01T01:30:50Z
dc.date.issued2020-10-03
dc.identifier.citationAzar, A.T. [et al.]. Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism. "International journal of computer applications in technology", 3 Octubre 2020, vol. 63, núm. 4, p. 273-285.
dc.identifier.issn1741-5047
dc.identifier.urihttp://hdl.handle.net/2117/331398
dc.description.abstractIn this paper, a novel control strategy based on an adaptive Self-Recurrent Wavelet Neural Network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists of two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and then the SRWNN weights are updated online by the adaptive control law. Apart from the hybrid control strategy proposed in this paper, a velocity observer is implemented to replace the use of velocity sensors. The outcomes obtained in the numerical experiment section prove that the smallest tracking error is obtained for the linear and angular displacements in comparison with other strategies found in literature due to the uncertainty and disturbance rejection properties of the sliding mode and the self-recurrent wavelet neural network controllers.
dc.format.extent13 p.
dc.language.isoeng
dc.publisherInderScience Publishers
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshAutomatic control
dc.subject.lcshSliding mode control
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshWavelets (Mathematics)
dc.subject.otherAdaptive wavelet neural networks
dc.subject.otherSliding mode control
dc.subject.otherSliding mode observer
dc.subject.otherSlider crank mechanism
dc.titleAdaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism
dc.typeArticle
dc.subject.lemacControl automàtic
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacOndetes (Matemàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.1504/IJCAT.2020.10032593
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::93 Systems Theory; Control
dc.relation.publisherversionhttps://www.inderscience.com/info/inarticle.php?artid=110404
dc.rights.accessOpen Access
local.identifier.drac29502700
dc.description.versionPostprint (author's final draft)
local.citation.authorAzar, Ahmad T.; Serrano, F.; Rossell, Josep M.; Vaidyanathan, S.; Zhu, Q.
local.citation.publicationNameInternational journal of computer applications in technology
local.citation.volume63
local.citation.number4
local.citation.startingPage273
local.citation.endingPage285


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