Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives
Primer Autor |
Ke, Dongliang
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Co-autores |
Wei, Yao
Wang, Fengxiang
Young, Hector
Rodriguez, Jose
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Título |
Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives
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Editorial |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Revista |
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
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Lenguaje |
en
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Resumen |
To eliminate the influence of the parameter mismatches and obtain high model quality, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed in this article and applied to the permanent magnet synchronous motor (PMSM) speed control system. Since the ARMA model group, which is a family of mathematical models containing AR, MA, and ARMA structures, considers operating states within several sampling periods to achieve better model accuracy, the plant is online-designed as this type, and its coefficients are estimated according to the sampled data by the normalized least-mean-square (NLMS) algorithm with adaptive normalized step length to achieve improved model quality with reduced calculation burden. Compared with the ultralocal MF-PCC strategy, the advantages of better stator current quality and robustness are demonstrated by the experimental results, as well as the reduced calculation burden compared with the recursive least square (RLS) algorithm used to estimate the coefficients.
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Fecha Publicación |
2023
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Tipo de Recurso |
artículo original
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doi |
10.1109/JESTPE.2023.3275562
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Formato Recurso |
PDF
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Palabras Claves |
Autoregressive moving average (ARMA) model group
data-driven model
model-free predictive current control (MF-PCC)
normalized least-mean-square (NLMS)
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Ubicación del archivo | |
Categoría OCDE |
Ingeniería
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Materias |
Grupo de modelos de media móvil autorregresiva (ARMA)
modelo basado en datos
control de corriente predictivo sin modelo (MF-PCC)
mínimos cuadrados normalizados (NLMS)
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Página de inicio (Recomendado-único) |
3874.0
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Página final (Recomendado-único) |
3884
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Identificador del recurso (Mandatado-único) |
artículo original
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Versión del recurso (Recomendado-único) |
versión publicada
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Derechos de acceso |
restringido
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Access Rights |
restringido
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Id de Web of Science |
WOS:001042129300024
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ISSN |
2168-6777
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Categoría WOS |
Ingeniería
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Referencia del Financiador (Mandatado si es aplicable-repetible) |
NSFC 52277070
FIPC 2022G026
FIPC 2020T3003
FIPC 2021I0039
FIPC 2022T3070
UFRO FRO19101
ANID FB0008
ANID 1210208
ANID 1221293
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