LAPSE:2023.19145
Published Article
LAPSE:2023.19145
An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches
Zehao Lyu, Xiang Wu, Jie Gao, Guojun Tan
March 9, 2023
Abstract
The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.
Keywords
parameter mismatch, permanent magnet synchronous motor, predictive current control
Subject
Suggested Citation
Lyu Z, Wu X, Gao J, Tan G. An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches. (2023). LAPSE:2023.19145
Author Affiliations
Lyu Z: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China
Wu X: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China [ORCID]
Gao J: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China
Tan G: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China
Journal Name
Energies
Volume
14
Issue
19
First Page
6342
Year
2021
Publication Date
2021-10-04
ISSN
1996-1073
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Original Submission
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PII: en14196342, Publication Type: Journal Article
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LAPSE:2023.19145
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https://doi.org/10.3390/en14196342
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