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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BPS1-0050-0092

Czasopismo

Przegląd Elektrotechniczny

Tytuł artykułu

Displacement Self-sensing of Bearingless Switched Reluctance Motors Based on LS-SVM

Autorzy Zhu, Z.  Sun, Y.  Huang, Y.  Ji, X. 
Treść / Zawartość http://pe.org.pl/
Warianty tytułu
PL Zastosowanie metody LS-SVM w określaniu przemieszczenia w bezłożyskowym silniku o przełączanej reluktancji BSRM
Języki publikacji EN
Abstrakty
EN To achieve the rotor radial displacement self-sensing for a bearingless switched reluctance motor (BSRM), a new displacement estimation method using least squares support vector machine (LS-SVM) was proposed. Firstly, the working principle and mathematic of a 3-phase 12/8 pole BSRM was introduced in brief. Then taking advantage of LS-SVM with better solution for small-sample learning problem and strong generalization ability, two LS-SVMs were trained off-line to obtain two efficient nonlinear mapping structures to express the dynamic behavior of BSRM. The LSSVM training data set is comprised of representative experimental data with current {i | i = (isa1, isa2, ima)} and rotor position θ as inputs and the corresponding displacements {D | D=(α , β )}as outputs. As well as giving a detailed explanation of the new method, simulation and experimental results were presented. It shows that the proposed LS-SVM-based displacement self-sensing method has high precision and operation efficiency.
PL W artykule przedstawiono uczący się estymator przesunięcia dla bezłożyskowego silnika o przełączanej reluktancji (BSRM), wykorzystujący metodę LS-SVM (ang. Least Square Support Vector Machines). Opisano zasadę działania i model matematyczny silnika BSRM 3- fazowego 12/8 biegunowego. W celu uzyskania efektywnej struktury mapowania nieliniowego do określenia stanów dynamicznych, zastosowano dwa algorytmy, które zostały nauczone offline. Estymator poddano weryfikacji symulacyjnej i eksperymentalnej.
Słowa kluczowe
PL silniki bezłożyskowe   silniki o przełączanej reluktancji   LS-SVM  
EN Bearingless Motors   switched reluctance motors   least squares support vector machine   Self-sensing  
Wydawca Wydawnictwo SIGMA-NOT
Czasopismo Przegląd Elektrotechniczny
Rocznik 2012
Tom R. 88, nr 12a
Strony 310--313
Opis fizyczny Bibliogr. 21 poz., rys., tab.
Twórcy
autor Zhu, Z.
autor Sun, Y.
autor Huang, Y.
autor Ji, X.
  • School of Electrical and Information Egineering, Jiangsu University, Zhenjiang 212013 China, ujszzy@gmail.com
Bibliografia
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[5] X. Cao, Z. Deng, G. Yang, Y. Yang. Mathematical model of bearingless switched reluctance motors based on maxwell stress tensor method, Proceedings of the CSEE, 29(2009): 78-83.
[6] Y. Sun, J Wu, Q. Xiang, The mathematic model of bearingless switched reluctance motor based on the finiteelement analysis, Proceedings of the CSEE, 27(2007): 33-40.
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[14] Y. Sun, Y. Zhou, X. Ji. Decoupling control of bearingless switched reluctance motor with neural network inverse system method, Proceeding of the CSEE, 31(2011): 117-123.
[15] C. A. Hudson, N. S. Lobo, R. Krishnan. Sensorless control of single switch-based switched reluctance motor drive using neural network, IEEE Trans. Ind. Electron,55(2008):321 - 329.
[16] E. Mese and D. A. Torrey, An approach for sensorless position estimation for switched reluctance motors using artifical neural networks, IEEE Trans. Ind. Electron, 17(2002): 66-75.
[17] V. Vapnik. New York: Springer-Verlag, (1999).
[18] J. A. K. Snykens, European Journal of Control, 7(2001).
[19] C. Xia, Z. He, Y. Zhou, X. Xie. Rotor position estimation for switched reluctance motors based on support vector machine Trans. China electro-technical Society, 22(2007):12-17.
[20] Z. Zhu, Y. Sun. Rotor displacement estimation for MB sensorless control, Przegląd Elektrotechniczny, 88(2012):141-145.
[21] Z. Zhu, Y. Sun, Y. Huang. Inverse dynamics modeling and control for bearingless switched reluctance motor, Electric Mach. and Contr., 15(2011):74-79.
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