HOME JOURNALS CONTACT

Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 14 | Page No.: 2760-2766
DOI: 10.3923/jas.2013.2760.2766
Speed Identification of Bearingless Induction Motor Based on Least Squares Support Vector Machine Inverse
Zebin Yang, Mingtao Wang, Xiaodong Sun and Huangqiu Zhu

Abstract: In order to achieve the online detection problem of rotor speed for Bearingless Induction Motor (BIM), a speed identification scheme based on the Least Square Support Vector Machine (LSSVM) inverse is presented in this study. According to the inherent relationship among the variables of BIM, the speed subsystem is first built and proved to be invertible. Secondly, the inverse model was constructed using LSSVM which has good function approximation characteristics. And then the obtained inverse model is combined with this subsystem, which well realized the real-time rotation speed identification. Finally, a vector control simulation platform of BIM is established to evaluate the proposed method. The simulation results demonstrates the proposed LSSVM inverse method can accurately identify the speed parameter in a full speed operation region with good dynamic and static performance.

Fulltext PDF

How to cite this article
Zebin Yang, Mingtao Wang, Xiaodong Sun and Huangqiu Zhu, 2013. Speed Identification of Bearingless Induction Motor Based on Least Squares Support Vector Machine Inverse. Journal of Applied Sciences, 13: 2760-2766.

Keywords: Bearingless induction motor (BIM), speed identification, least squares support vector machine (LSSVM) and inverse vector control

REFERENCES
  • Bartholet, M.T., T. Nussbaumer and J.W. Kolar, 2011. Comparison of voltage-source inverter topologies for two-phase bearingless slice motors. IEEE Trans. Ind. Electr., 58: 1921-1925.
    CrossRef    

  • Rodriguez, E.F. and J.A. Santisteban, 2011. An improved control system for a split winding bearingless induction motor. IEEE Trans. Ind. Electr., 58: 3401-3408.
    CrossRef    

  • Sun, X.D. and H.Q. Zhu, 2010. Decoupling control of bearingless induction motors based on neural network inverse system method. Trans. China Electrotechnical Soc., 25: 43-49.

  • Chiba, A., T. Fukao, O. Ichikawa, M. Oshima, M. Takemoto and D.G. Dorrell, 2005. Magnetic Bearings and Bearingless Drives. Elsevier Newnes Press, Boston, MA., ISBN: 9780080478975, Pages: 400

  • Chiba, A. and J. Asama, 2012. Influence of rotor skew in induction type bearingless motor. IEEE Trans. Magnet., 48: 4646-4649.
    CrossRef    

  • Schuhmann, T., W. Hofmann and R. Werner, 2012. Improving operational performance of active magnetic bearings using Kalman filter and state feedback control. IEEE Trans. Ind. Electr., 59: 821-829.
    CrossRef    

  • Fang, J., S. Zheng and B. Han, 2012. Attitude sensing and dynamic decoupling based on active magnetic bearing of MSDGCMG. IEEE Trans. Instrumentation Measurement, 61: 338-348.
    CrossRef    

  • Chiba, A. and J.A. Santisteban, 2012. A PWM harmonics elimination method in simultaneous estimation of magnetic field and displacements in bearingless induction motors. IEEE Trans. Ind. Appli., 48: 124-131.
    CrossRef    

  • Wang, L.P., H.G. Zhang and X.C. Liu, 2012. Integral backstepping controller in the sensorless vector-control system for permanent magnet synchronous motor. Control Theory Appli., 29: 199-204.

  • Chen, Z., M. Tomita, S. Doki and S. Okuma, 2003. An extended electromotive force model for sensorless control of interior permanent-magnet synchronous motors. IEEE Trans. Ind. Electr., 50: 288-295.
    CrossRef    

  • Schauder, C., 1992. Adaptive speed identification for vector control of induction motors without rotational transducers. IEEE Trans. Ind. Appl., 28: 1054-1061.
    CrossRef    Direct Link    

  • Gadoue, S.M., D. Giaouris and J.W. Finch, 2010. MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy logic adaptation mechanisms. IEEE Trans. Energy Conversion, 25: 394-402.
    CrossRef    

  • Cirrincione, M. and M. Pucci, 2005. Sensorless direct torque control of an induction motor by a TLS-based MRAS observer with adaptive integration. Automatica, 41: 1843-1854.
    Direct Link    

  • Shi, K.L., T.F. Chan, Y.K. Wong and S.L. Ho, 2002. Speed estimation of an induction motor drive using an optimized extended Kalman filter. Ind. Electronics IEEE Trans., 49: 124-133.
    CrossRef    Direct Link    

  • Salvatore, N., A. Caponio, F. Neri, S. Stasi and G.L. Cascella, 2010. Optimization of delayed-state kalman-filter-based algorithm via differential evolution for sensorless control of induction motors. IEEE Trans. Ind. Electr., 57: 385-394.
    CrossRef    

  • Kim, S.K. and J.K. Seok, 2011. High-frequency signal injection-based rotor bar fault detection of inverter-fed induction motors with closed rotor slots. IEEE Trans. Ind. Appli., 47: 1624-1631.
    CrossRef    

  • Huang, Y.H., Y.K. Sun, B. Wang, X.G. Zhu and C.G. Xia, 2010. Research of soft sensor based on fuzzy neural network inverse system for lysine fermentation process. Chin. J. Scientific Instrument, 31: 862-865.

  • Zhang, S.N., F.N. Wang, D.K. He and J. Runda, 2010. Soft sensing prcobalt oxalate particle size based on multiple LS-SVM regssion. Chin. J. Scientific Instrument, 31: 2081-2087.

    • © Science Alert. All Rights Reserved