DocumentCode
499013
Title
Permanent magnet synchronous motor direct torque control with zero vector based on inteligent method
Author
Li, Guang-ye ; Wan, Jian-ru ; Liu, Ying-pei ; Hong, Shen ; Yuan, Chen-hu
Author_Institution
Inst. of Electr. & Autom. Eng., Tianjin Univ., Tianjin, China
Volume
2
fYear
2009
fDate
12-15 July 2009
Firstpage
755
Lastpage
760
Abstract
There are many torque ripples in traditional direct torque control (DTC) for permanent magnet synchronous motor(PMSM) drive, which are caused by the error of flux linkage estimation and hysteresis band amplitudes, etc.To solve these problems , a radial basis function (RBF) neural networks was designed to estimate the stator flux linkage, and fuzzy controller, in which zero-voltage vector is used, was designed to replace the traditional hysteresis controllers. Therefore, intelligence DTC strategy was accomplished. concerted control between flux linkage and torque is realized. The flux linkage error and torque error are classified reasonably by fuzzy controller and electromagnet torque ripples are dramatically reduced. Meanwhile, rapid torque response and strong robustness of direct torque control method are still maintained. The simulation and experiment results have verified the feasibility and effectiveness of this method.
Keywords
control system synthesis; fuzzy control; machine control; permanent magnet motors; radial basis function networks; synchronous motors; torque control; direct torque control; electromagnet torque ripples; flux linkage error; flux linkage estimation; fuzzy controller; hysteresis band amplitudes; intelligent method; permanent magnet synchronous motor; radial basis function neural networks; stator flux linkage; torque error; zero vector; Amplitude estimation; Couplings; Error correction; Fuzzy control; Fuzzy neural networks; Hysteresis; Neural networks; Permanent magnet motors; Stators; Torque control; Direct Torque Control (DTC); Fuzzy Control; Permanent Magnet Synchronous Motor (PMSM); RBF Neural Networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212463
Filename
5212463
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