Vector Control of Permanent Magnet Synchronous Motor Based on Parameter Identification Technique

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Abstract:

Real-time accuracy of the permanent magnet synchronous motor (PMSM) parameter directly affects the performance of the servo system. In this paper, the model reference adaptive parameter identification method, the parameters of permanent magnet synchronous motor running as the actual model , the mathematical model derived from the equation of state in the d, q coordinate system as the theoretical model , in order to calculate the real-time stator resistance , inductance and rotor flux value . Thereby increasing the accuracy of the vector control .Using simulink simulation results show that, The system can quickly and accurately calculate the real-time parameters and the applied control.

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705-708

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September 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Chen Zhenfeng, Li Yanru, Li Jie, RLS -based embedded permanent magnet synchronous motor parameter identification technology , Journal of Xi'an University of Technology , Vol. 25,No. 3,(2009).

Google Scholar

[2] Boileau T. Nahid-Mobarakeh , B. Meibody-Tabar . On-line Identification of PMSM Parameters:Model-Reference vs EKF , October5-9, 2008, Edmonton, Canada. 2008:1-8.

DOI: 10.1109/08ias.2008.176

Google Scholar

[3] Sun Pindong, Gu Xuefeng , Zhu Zhiqiang, Zhu Xi, Brushless synchronous motor parameter identification algorithm based on EKF AC PM , Journal of Nanjing Normal University , Vol. 8,No. 1,2008. 3.

Google Scholar

[4] Samuel J. Underwood, Iqbal Husain. Online Parameter Estimation and Adaptive Control of Permanent-Magnet Synchronous Machines. 2435-2443 (2010).

DOI: 10.1109/tie.2009.2036029

Google Scholar

[5] Zhao Li, Guo Qiujian, Zhao Feng, Interior permanent magnet synchronous motor inductance parameter identification , (2008).

Google Scholar