Real-time Nonlinear Modeling Of A Twin Rotor Mimo System Using Evolving Neuro-fuzzy Network

dc.creatorSilva A.
dc.creatorCaminhas W.
dc.creatorLemos A.
dc.creatorGomide F.
dc.date2015
dc.date2015-06-25T12:55:34Z
dc.date2015-11-26T15:19:15Z
dc.date2015-06-25T12:55:34Z
dc.date2015-11-26T15:19:15Z
dc.date.accessioned2018-03-28T22:28:46Z
dc.date.available2018-03-28T22:28:46Z
dc.identifier9781479945313
dc.identifierIeee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Cica 2014: 2014 Ieee Symposium On Computational Intelligence In Control And Automation, Proceedings. Institute Of Electrical And Electronics Engineers Inc., v. , n. , p. - , 2015.
dc.identifier
dc.identifier10.1109/CICA.2014.7013229
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84922874901&partnerID=40&md5=3304855b9a082a893a4b89d1a454c984
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/85617
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85617
dc.identifier2-s2.0-84922874901
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1259698
dc.descriptionThis paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.
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dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICA 2014: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, Proceedings
dc.rightsfechado
dc.sourceScopus
dc.titleReal-time Nonlinear Modeling Of A Twin Rotor Mimo System Using Evolving Neuro-fuzzy Network
dc.typeActas de congresos


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