Documents disponibles écrits par cet auteur

MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy-logic adaptation mechanisms / Gadoue, S.M. in IEEE transactions on energy conversion, Vol. 25 N° 2 (Juin 2010) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 25 N° 2 (Juin 2010) . - pp. 394 - 402 Titre : MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy-logic adaptation mechanisms Type de document : texte imprimé Auteurs : Gadoue, S.M., Auteur ; Giaouris, Damian, Auteur ; Finch, J.W., Auteur Année de publication : 2010 Article en page(s) : pp. 394 - 402 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : fuzzy  control;  induction  motors;  machine  vector  control;  model  reference  adaptive  control  systems;  rotors;  variable  structure  systems Résumé : In this paper, two novel adaptation schemes are proposed to replace the classical PI controller used in model reference adaptive speed-estimation schemes that are based on rotor flux. The first proposed adaptation scheme is based on sliding-mode theory. A new speed-estimation adaptation law is derived using Lyapunov theory to ensure estimation stability, as well as fast error dynamics. The other adaptation mechanism is based on fuzzy-logic strategy. A detailed experimental comparison between the new and conventional schemes is carried out in both open- and closed-loop sensorless modes of operation when a vector control drive is working at very low speed. Superior performance has been obtained using the new sliding-mode and fuzzy-logic adaptation mechanisms in both modes of operations. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5357459&sortType%3Das [...] [article] MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy-logic adaptation mechanisms [texte imprimé] / Gadoue, S.M., Auteur ; Giaouris, Damian, Auteur ; Finch, J.W., Auteur . - 2010 . - pp. 394 - 402. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 25 N° 2 (Juin 2010) . - pp. 394 - 402 Mots-clés : fuzzy  control;  induction  motors;  machine  vector  control;  model  reference  adaptive  control  systems;  rotors;  variable  structure  systems Résumé : In this paper, two novel adaptation schemes are proposed to replace the classical PI controller used in model reference adaptive speed-estimation schemes that are based on rotor flux. The first proposed adaptation scheme is based on sliding-mode theory. A new speed-estimation adaptation law is derived using Lyapunov theory to ensure estimation stability, as well as fast error dynamics. The other adaptation mechanism is based on fuzzy-logic strategy. A detailed experimental comparison between the new and conventional schemes is carried out in both open- and closed-loop sensorless modes of operation when a vector control drive is working at very low speed. Superior performance has been obtained using the new sliding-mode and fuzzy-logic adaptation mechanisms in both modes of operations. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5357459&sortType%3Das [...]

Sensorless control of induction motor drives at very low and zero speeds using neural network flux observers / Gadoue, Shady M. in IEEE transactions on industrial electronics, Vol. 56 N° 8 (Août 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N° 8 (Août 2009) . - pp. 3029 - 3039 Titre : Sensorless control of induction motor drives at very low and zero speeds using neural network flux observers Type de document : texte imprimé Auteurs : Gadoue, Shady M., Auteur ; Giaouris, Damian, Auteur ; Finch, John W., Auteur Article en page(s) : pp. 3029 - 3039 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Flux  estimation  Induction  motor  Model  reference  adaptive  system  (MRAS)  Neural  networks  (NNs)  Sensorless  control Résumé : A new method is described which considerably improves the performance of rotor flux model reference adaptive system (MRAS)-based sensorless drives in the critical low and zero speed regions of operation. It is applied to a vector-controlled induction motor drive and is experimentally verified. The new technique uses an artificial neural network (NN) as a rotor flux observer to replace the conventional voltage model. This makes the reference model free of pure integration and less sensitive to stator resistance variations. This is a radically different way of applying NNs to MRAS schemes. The data for training the NN are obtained from experimental measurements based on the current model avoiding voltage and flux sensors. This has the advantage of considering all drive nonlinearities. Both open- and closed-loop sensorless operations for the new scheme are investigated and compared with the conventional MRAS speed observer. The experimental results show great improvement in the speed estimation performance for open- and closed-loop operations, including zero speed. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5071300 [article] Sensorless control of induction motor drives at very low and zero speeds using neural network flux observers [texte imprimé] / Gadoue, Shady M., Auteur ; Giaouris, Damian, Auteur ; Finch, John W., Auteur . - pp. 3029 - 3039. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N° 8 (Août 2009) . - pp. 3029 - 3039 Mots-clés : Flux  estimation  Induction  motor  Model  reference  adaptive  system  (MRAS)  Neural  networks  (NNs)  Sensorless  control Résumé : A new method is described which considerably improves the performance of rotor flux model reference adaptive system (MRAS)-based sensorless drives in the critical low and zero speed regions of operation. It is applied to a vector-controlled induction motor drive and is experimentally verified. The new technique uses an artificial neural network (NN) as a rotor flux observer to replace the conventional voltage model. This makes the reference model free of pure integration and less sensitive to stator resistance variations. This is a radically different way of applying NNs to MRAS schemes. The data for training the NN are obtained from experimental measurements based on the current model avoiding voltage and flux sensors. This has the advantage of considering all drive nonlinearities. Both open- and closed-loop sensorless operations for the new scheme are investigated and compared with the conventional MRAS speed observer. The experimental results show great improvement in the speed estimation performance for open- and closed-loop operations, including zero speed. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5071300