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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 34, Number 5(2) (2022)
Copyright(C) MYU K.K.
pp. 1779-1789
S&M2928 Research Paper of Special Issue
https://doi.org/10.18494/SAM3748
Published: May 17, 2022

Adaptive Speed Prediction for Direct Torque-controlled Permanent Magnet Synchronous Motor Drive Using Elephant Herding Optimization Algorithm [PDF]

Yung-Chang Luo, Yan-Xun Peng, Chia-Hung Lin, and Ying-Piao Kuo

(Received November 25, 2021; Accepted February 28, 2022)

Keywords: direct torque-controlled (DTC) permanent magnet synchronous motor (PMSM) drive, speed prediction, model reference adaptive control (MRAC), elephant herding optimization (EHO) algorithm

In this study, an adaptive speed prediction scheme based on reactive power was established for direct torque-controlled (DTC) permanent magnet synchronous motor (PMSM) drives. The current and flux of a stator were used to establish a DTC PMSM drive. Hall effect current sensors with a non-contact sensing technique were used to detect the stator current of the PMSM. The voltage space vector pulse width modulation (VSVPWM) DTC scheme was used in place of a traditional switching table (ST) DTC scheme to reduce current and torque ripples. Model reference adaptive control (MRAC) was utilized to develop a speed prediction scheme, and its adaptation mechanism was designed using the elephant herding optimization (EHO) algorithm. The torque, flux, and speed controllers were designed using a proportional–integral (P–I)-type controller. The MATLAB/Simulink© toolbox was used to establish the simulation scheme, and all control algorithms were realized using a microprocessor control card. The simulation and experimental results confirmed the effectiveness of the proposed approach.

Corresponding author: Yung-Chang Luo


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Cite this article
Yung-Chang Luo, Yan-Xun Peng, Chia-Hung Lin, and Ying-Piao Kuo, Adaptive Speed Prediction for Direct Torque-controlled Permanent Magnet Synchronous Motor Drive Using Elephant Herding Optimization Algorithm, Sens. Mater., Vol. 34, No. 5, 2022, p. 1779-1789.



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