Warianty tytułu
Estymacja położenia wirnika w maszynach z poduszką magnetyczną
Języki publikacji
Abstrakty
This paper presents an on-line recursive least squares support vector machine(O-RLS-SVM)-based displacement estimator for magnetic bearings (MBs). The basic premise of the method is that an O-RLS-SVM forms an efficient mapping structure for a nonlinear MB. Through the measurement of phase flux linkages and currents, the O-RLS-SVM is able to estimate the rotor displacement; thereby it facilitates the elimination of the rotor displacement sensor. Simulation results show that the estimator has high estimation precision and favourable operation efficiency.
W artykule przedstawiono system estymacji położenia wirniku w maszynach z poduszką magnetyczną. System nie wymaga czujników – miarą położenia wirnika są przesunięcia fazowe prądu i strumienia magnetycznego.
Czasopismo
Rocznik
Tom
Strony
141-145
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
autor
autor
autor
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013 China, ujszzy@gmail.com
Bibliografia
- [1] Bronislaw Tomczuk, Andrzej Waindok, Jan Zimon, Field Calculation of Electro-magnetic Parameters for Linear Motors and Magnetic Bearings, Przeglad Elektrotechniczny, 85(2009) No.3, 111-114.
- [2] Daniel Martsa, Miklos Kuczmann, Optimization and Finite Element Analysis of 3-Pole Magnetic Bearing with Nonlinear Material, Przeglad Elektrotechniczny, 86(2010) No.12, 91-94.
- [3] Albritton N. G., and Hung J. Y., Observers for Sensorless Control of Industrial Magnetic Bearings, International Conference on Industrial Electronics, Control, and Instrumentation, 2 (1995), 973-978.
- [4] Matsuda K., Kijimoto S., and Kanemitsu Y., Self-sensing Three- Pole Magnetic Bearing Using A Kalman Filter, SICE-ICASE International Joint Conference, (2006), 1590-1594.
- [5] Mese E., Torrey D. A., An Approach for Sensorless Position Estimation for Switched Reluctance Motors Using Artifical Neural Networks, IEEE Transactions on Power Electronics, 17(2002), No. 1, 66-75.
- [6] Vapnik V, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1999.
- [7] Suykens J. A. K., and Vandewalle J., Recurrent Least Squares Support Vector Machines, IEEE Transactions on Circuits and Systems I, 47(2000), No. 7, 1109-1114.
- [8] Suykens J. A. K., Support Vector Machines: A Nonlinear Modeling and Control Perspective. European Journal of Control, 7(2001),311-327.
- [9] Sun Yukun, Zhu Zhiying. Inverse-Model Identification and Decoupling Control Based on Least Squares Support Vector Machine for 3 Degree-of-Freedom Hybrid Magnetic Bearing, Proceedings of the Chinese Society of Electrical Engineering, 30(2010), No. 15, 112-117.
- [10] Ji Shanghua, Zhang Weiyu, Huang Zhenyue, Zhu Huangqiu, Parameter design and optimization of AC active magnetic bearing, Proceedings of the Chinese Society of Electrical Engineering, 31(2011), No.21, 150-157.
- [11] Huang Zhenyue, Zou Haidan, Zhu Huangqiu, Li Tianbo, Simulation of Self-sensing Technique for 3-phase AC Active Magnetic Bearings, Proceedings of the Chinese Society of Electrical Engineering, (2009), No.S1, 228-233.
- [12] Shyh-Leh Chen,Cheng-Chi Weng, Robust Control of a Voltage- Controlled Three-Pole Active Magnetic Bearing System, IEEE/ ASME Transactions on Mechatronics, 15(2010), No.3, 381-388.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0049-0031