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Sensors and Materials, Volume 33, Number 6(2) (2021)
Copyright(C) MYU K.K.
pp. 1945-1955
S&M2585 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3271
Published: June 9, 2021

Field-oriented Controlled Permanent Magnet Synchronous Motor Drive with Dynamic-parameter Speed Controller Based on Generalized Regression Neural Network [PDF]

Yung-Chang Luo, Hsu-Hung Zheng, Chia-Hung Lin, and Ying-Piao Kuo

(Received December 29, 2020; Accepted March 5, 2021)

Keywords: dynamic control parameters, field-oriented controlled (FOC), permanent magnet synchronous motor (PMSM) drive, generalized regression neural network (GRNN), firefly algorithm (FA)

A dynamic-parameter speed controller based on a generalized regression neural network (GRNN) was developed for a field-oriented controlled (FOC) permanent magnet synchronous motor (PMSM) drive. The decoupled FOC PMSM drive was established using the current and voltage of the stator. The designed time-varying-parameters speed controller replaced the conventional fixed-parameters speed controller to adapt to drastic load variations and serious interference. A GRNN was utilized to develop the time-varying-parameters speed controller, and the smooth curve of the pattern layer was adjusted using the firefly algorithm (FA). Hall effect current sensors were used as an electromagnetic sensing element to detect the stator current from the PMSM. The MATLAB/Simulink© toolbox was used to establish the simulation scheme, and all the control algorithms were realized using a TI DSP 6713-and-F2812 control card. Simulation and experimental results under load changes confirmed the effectiveness of the proposed approach.

Corresponding author: Yung-Chang Luo


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Cite this article
Yung-Chang Luo, Hsu-Hung Zheng, Chia-Hung Lin, and Ying-Piao Kuo, Field-oriented Controlled Permanent Magnet Synchronous Motor Drive with Dynamic-parameter Speed Controller Based on Generalized Regression Neural Network, Sens. Mater., Vol. 33, No. 6, 2021, p. 1945-1955.



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