Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDescriptor-type kalman filter and TLS EXIN speed estimate for sensorless control of a linear induction motor
Anno di pubblicazione2014
FormatoElettronico
Autore/iAlonge F.; Cirrincione M.; D'Ippolito F.; Pucci M.; Sferlazza A.; Vitale G.
Affiliazioni autoriDepartment of Energy Information Engineering and Mathematical Models, University of Palermo, Palermo, 90128, Italy; School of Engineering and Physics (SEP), University of the South Pacific, Laucala Campus, Suva, Fiji; University of Technology of Belfort Montbeliard, Belfort, 90400, France; Laboratoires OPERA/FCLAB FRCNRS3539, Belfort, 90010, France; Institute on Intelligent Systems for Automation, Section of Palermo, National Research Council of Italy, Palermo, 90128, Italy; Institute of Intelligent Systems for Automation, National Research Council of Italy, Palermo, 90128, Italy
Autori CNR e affiliazioni
  • GIANPAOLO VITALE
  • MARCELLO PUCCI
Lingua/e
  • inglese
AbstractThis paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine. The end effects of the LIMs have been considered an uncertainty treated by the KF. The TLS EXIN neuron has been used to compute, in recursive form, the machine linear speed online since it is the only neural network able to solve online, in a recursive form, a TLS problem. The proposed KF TLS speed estimator has been tested experimentally on a suitably developed test setup, and it has been compared with the classic extended KF.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da3754
Pagine a3766
Pagine totali12
RivistaIEEE transactions on industry applications
Attiva dal 1972
Editore: Institute of Electrical and Electronic Engineers] - [New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0093-9994
Titolo chiave: IEEE transactions on industry applications
Titolo proprio: IEEE transactions on industry applications.
Titolo abbreviato: IEEE trans. ind. appl.
Titoli alternativi:
  • Transactions on industry applications
  • Industry applications
Numero volume della rivista50
Fascicolo della rivista6
DOI10.1109/TIA.2014.2316367
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84913534232)
Parole chiaveKalman filter (KF), linear induction motor (LIM), neural networks (NNs), total least squares (TLS)
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84913534232&partnerID=q2rCbXpz
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioniquartile: Q1
Strutture CNR
  • ISSIA — ISSIA - Sede secondaria di Palermo
Moduli/Attività/Sottoprogetti CNR
  • SP.P03.011.001 : Convertitori, attuatori e azionamenti elettrici
Progetti Europei-
Allegati
Descriptor-Type Kalman Filter and TLS EXIN Speed Estimate for Sensorless Control of a Linear Induction Motor (documento privato )
Descrizione: This paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system.
Tipo documento: application/pdf

Dati associati a vecchie tipologie
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Editore
  • IEEE - Institute of Electrical and Electronics Engineers, Piscataway, N.J. (Stati Uniti d'America)