Improved Finite-Control-Set Model Predictive Control With Virtual Vectors for PMSHM Drives
Finite-control-set model predictive current control (FCS-MPCC) always has large steady-state fluctuation and computational burden. In this paper, a novel FCS-MPCC without a modulator to drive permanent magnet synchronous hub motors (PMSHMs), which combines virtual vectors expansion scheme and duty c...
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Published in | IEEE transactions on energy conversion Vol. 37; no. 3; pp. 1885 - 1894 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Finite-control-set model predictive current control (FCS-MPCC) always has large steady-state fluctuation and computational burden. In this paper, a novel FCS-MPCC without a modulator to drive permanent magnet synchronous hub motors (PMSHMs), which combines virtual vectors expansion scheme and duty cycle control was proposed. The lack of a modulator reduces the complexity of the control system. The virtual vectors are synthesized by using active vectors, which improve the accuracy of voltage selection, and further improve PMSHM's steady-state performance and reduce current harmonics. The duty cycle control uses a zero vector to obtain better steady-state performance. However, the duty cycle of the virtual vectors is limited by the synthesis method, and further analysis is needed. A new calculation process is proposed to reduce the amount of calculation. The deadbeat principle is used to get reference voltage which determines sectors. Then, the best voltage vector in the selected sector is determined by the predetermined cost function. The traditional MPCC and the duty cycle MPCC (DCMPCC) are used as a comparison item to compare with the proposed method to illustrate its effectiveness. Results confirm that improved MPCC has good steady-state performance while maintaining a fast dynamic response. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0885-8969 1558-0059 |
DOI: | 10.1109/TEC.2021.3138905 |