Model-Free Predictive Current Control for PMSM Drives Using an Improved Dynamic Linearization Data Model

Model predictive control has attracted much attention in electric drives, but its parameter sensitivity on explicit models poses inherent challenges to the further application. This paper proposes an improved model-free predictive current control (MFPCC) based on a full-form dynamic linearization (F...

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Vydáno v:IEEE transactions on energy conversion s. 1 - 12
Hlavní autoři: Zhang, Kai, Lu, Junyong, Liu, Yingquan, Ma, Jien, Qiu, Lin, Liu, Xing, Fang, Youtong
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Accuracy
Data models
Dynamic Linearization (DL)
Estimation
Mathematical models
Model-free predictive control (MFPC)
Motors
permanent magnet synchronous motor (PMSM)
Prediction algorithms
Predictive control
Predictive models
Stator windings
Vectors
ISSN:0885-8969, 1558-0059
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Shrnutí:Model predictive control has attracted much attention in electric drives, but its parameter sensitivity on explicit models poses inherent challenges to the further application. This paper proposes an improved model-free predictive current control (MFPCC) based on a full-form dynamic linearization (FFDL) data model. Through the pseudo gradient (PG) and estimation algorithm, the FFDL modeling method only uses the input-output data of the system. However, the accuracy of the classic FFDL data model relies heavily on the PG initial value, which is related to the plant. In order to reduce the initial value dependence, this paper further improves the FFDL technique with a diagonal gain matrix, while the stability analysis and parameter design guidance are given. With only simple parameter design, the proposed method can establish a prediction model based on the input-output data to deal with uncertain systems. Finally, the performance of the proposed method is verified on a PMSM experimental platform with a three-level neutral-point-clamped inverter.
ISSN:0885-8969
1558-0059
DOI:10.1109/TEC.2025.3596088