The Research on Permanent Magnetic Synchronous Motor Vector Control Based on MRAS
The precise speed and torque controls of Permanent Magnetic Synchronous Motor (PMSM) are usually realized by using speed or position sensor. However, the mounting of speed or position sensor requires an additional space. The cost of motor drive system with speed or position sensor is high, the relia...
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Published in | Applied Mechanics and Materials Vol. 719-720; no. Materials and Engineering Technology; pp. 381 - 387 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Zurich
Trans Tech Publications Ltd
01.01.2015
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Subjects | |
Online Access | Get full text |
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Summary: | The precise speed and torque controls of Permanent Magnetic Synchronous Motor (PMSM) are usually realized by using speed or position sensor. However, the mounting of speed or position sensor requires an additional space. The cost of motor drive system with speed or position sensor is high, the reliability is low and is difficult to maintain. This paper presents one novel control strategy for PMSM sensorless vector control based on model reference adaption system(MRAS). This control strategy doesn’t need any speed or position sensor and can estimate the rotor speed with a few parameters. In this paper, PMSM itself is selected as reference model, and the mathematical model of PMSM which includes estimated parameter is regarded as adjustable model. The output error of these two models is used to drive the adaption mechanism and the estimated speed is obtained. The simulation results verify the proposed control strategy is effective, it has excellent dynamic and stable responses, the estimated speed precision is high and the system is robust. |
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Bibliography: | Selected, peer reviewed papers from the 2014 International Conference on Materials and Engineering Technology (MET 2014), October 24-26, 2014, Chicago, USA ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISBN: | 9783038353843 3038353841 |
ISSN: | 1660-9336 1662-7482 1662-7482 |
DOI: | 10.4028/www.scientific.net/AMM.719-720.381 |