Deep-neural-network-based Electromagnetic Analysis and Optimal Design of Fractional-slot Brushless DC Motor for High Torque Robot Joints

Fractional-slot brushless DC motors (FS-BLDCMs) have the advantages of high torque density and low cogging torque for robot joints. However, finite element analysis (FEA) of the FS-BLDCMs causes time consumption, which obstructs the progress on finding optimal electromagnetic characteristics of the...

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Bibliographic Details
Published in2023 3rd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT) pp. 715 - 719
Main Authors Liu, Anguo, Meng, Fei, Hu, Hengzai
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.07.2023
Subjects
Artificial neural networks
Brushless DC motors
Deep Neural Network
Electromagnetic analysis
Finite element analysis
Forging
Fractional-slot Brushless DC Motor
High Torque Density Motor
Mechatronics
Motor Optimization
Torque
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Summary:Fractional-slot brushless DC motors (FS-BLDCMs) have the advantages of high torque density and low cogging torque for robot joints. However, finite element analysis (FEA) of the FS-BLDCMs causes time consumption, which obstructs the progress on finding optimal electromagnetic characteristics of the FS-BLDCMs. This paper presents a novel design method to improve the FS-BLDCM motor characteristics and improve the computation efficiency by the deep neural network (DNN). The FS-BLDCM motor performance is optimized by the genetic algorithm and validated by finite element analysis. The computation time between the finite element analysis (FEA) and the deep neural network (DNN) is compared. The result shows the efficiency of the deep neural network.
DOI:10.1109/ICEEMT59522.2023.10262957