A Novel Three-Vector-Based Model Predictive Flux Control With Low Computation Complexity for SPMSM

A novel three-vector-based model predictive flux control (TV-MPFC) with low computation complexity and low stator flux ripple for SPMSM is proposed, in which the stator flux vector tracking error is evaluated in the cost function, so the weighting factor used in model predictive torque control (MPTC...

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Veröffentlicht in:IEEE transactions on transportation electrification Jg. 10; H. 2; S. 3956 - 3965
Hauptverfasser: Lu, Kejin, Li, Xianglin, Zhao, Yujian, Yi, Peng, Yan, Bo, Hua, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2332-7782, 2577-4212, 2332-7782
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Zusammenfassung:A novel three-vector-based model predictive flux control (TV-MPFC) with low computation complexity and low stator flux ripple for SPMSM is proposed, in which the stator flux vector tracking error is evaluated in the cost function, so the weighting factor used in model predictive torque control (MPTC) is eliminated, thus avoiding tedious tuning work. To enhance the steady-state performance, three voltage vectors including two active vectors and one zero vector are applied in one period, and the key improvement of the proposed strategy is to construct a novel voltage vector selection table for the second optimal active vector based on the stator flux vector error which decreases the number of candidate active vectors, thus reducing the computation complexity. The single-vector-based MPFC (SV-MPFC) and four three-vector-based model predictive control (MPC) algorithms are compared in this article. The comparative experimental tests are finally implemented and the results show that the proposed TV-MPFC can enhance steady-state performance of stator flux while reducing computation complexity, hence it is more suitable for scenarios with higher requirement on stator flux.
Bibliographie:ObjectType-Article-1
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ISSN:2332-7782
2577-4212
2332-7782
DOI:10.1109/TTE.2023.3315522