MRAS Speed Observer for High-Performance Linear Induction Motor Drives Based on Linear Neural Networks (Articolo in rivista)

Type
Label
  • MRAS Speed Observer for High-Performance Linear Induction Motor Drives Based on Linear Neural Networks (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TPEL.2012.2200506 (literal)
Alternative label
  • Accetta Angelo, Cirrincione Maurizio, Pucci Marcello, Gianpaolo Vitale. (2013)
    MRAS Speed Observer for High-Performance Linear Induction Motor Drives Based on Linear Neural Networks
    in IEEE transactions on power electronics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Accetta Angelo, Cirrincione Maurizio, Pucci Marcello, Gianpaolo Vitale. (literal)
Pagina inizio
  • 123 (literal)
Pagina fine
  • 134 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • quartile: Q1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203599 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 28 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 11 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Accetta Angelo: CNR - ISSIA UOS di Palermo Cirrincione Maurizio: Universit´e Technologique de Belfort Montbeliard, 90010 Belfort, France (e-mail: m.cirrincione@ieee.org). (literal)
Titolo
  • MRAS Speed Observer for High-Performance Linear Induction Motor Drives Based on Linear Neural Networks (literal)
Abstract
  • This paper proposes a neural network (NN) model reference adaptive system (MRAS) speed observer suited for linear induction motor (LIM) drives. The voltage and current flux models of the LIM in the stationary reference frame, taking into consideration the end effects, have been first deduced. Then, the induced part equations have been discretized and rearranged so as to be represented by a linear NN (ADALINE). On this basis, the transport layer security EXIN neuron has been used to compute online, in recursive form, the machine linear speed. The proposed NN MRAS observer has been tested experimentally on suitably developed test set-up. Its performance has been further compared to the classic MRAS and the sliding- (literal)
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