Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives

Primer Autor
Ke, Dongliang
Co-autores
Wei, Yao
Wang, Fengxiang
Young, Hector
Rodriguez, Jose
Título
Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives
Editorial
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Revista
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
Lenguaje
en
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.
Fecha Publicación
2023
Tipo de Recurso
artículo original
doi
10.1109/JESTPE.2023.3275562
Formato Recurso
PDF
Palabras Claves
Autoregressive moving average (ARMA) model group
data-driven model
model-free predictive current control (MF-PCC)
normalized least-mean-square (NLMS)
Ubicación del archivo
Categoría OCDE
Ingeniería
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)
Página de inicio (Recomendado-único)
3874.0
Página final (Recomendado-único)
3884
Identificador del recurso (Mandatado-único)
artículo original
Versión del recurso (Recomendado-único)
versión publicada
Derechos de acceso
restringido
Access Rights
restringido
Id de Web of Science
WOS:001042129300024
ISSN
2168-6777
Categoría WOS
Ingeniería
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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