Repositorio
Institucional

Ke, Dongliang

Wei, Yao

Wang, Fengxiang

Young, Hector

Rodriguez, Jose

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

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS

en

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.

2023

artículo original

10.1109/JESTPE.2023.3275562

PDF

Autoregressive moving average (ARMA) model group

data-driven model

model-free predictive current control (MF-PCC)

normalized least-mean-square (NLMS)

Ingeniería

Grupo de modelos de media móvil autorregresiva (ARMA)

modelo basado en datos

control de corriente predictivo sin modelo (MF-PCC)

mínimos cuadrados normalizados (NLMS)

3874.0

3884

artículo original

versión publicada

restringido

restringido

WOS:001042129300024

2168-6777

Ingeniería

NSFC 52277070

FIPC 2022G026

FIPC 2020T3003

FIPC 2021I0039

FIPC 2022T3070

UFRO FRO19101

ANID FB0008

ANID 1210208

ANID 1221293