Open AccessTorque Ripple Control Strategy of Switched Reluctance Motor Based on BP Neural Network
Author(s) -
Junxin Xu,
Chaozhi Huang,
Wensheng Cao,
Yuliang Wu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2242/1/012036
Subject(s) - switched reluctance motor , control theory (sociology) , torque ripple , direct torque control , torque , artificial neural network , computer science , torque motor , reluctance motor , matlab , control engineering , engineering , control (management) , artificial intelligence , induction motor , physics , voltage , electrical engineering , thermodynamics , operating system
Switched reluctance motor (SRM) has many advantages, but when the motor is running, the fixed turn-on angle will cause the torque ripple of the motor at different speeds and loads. Therefore, an torque ripple control strategy of switched reluctance motor based on BP neural network is proposed. Firstly, the nonlinear relationship among speed, load torque and opening angle is established by using the fitting generalization ability of BP neural network. In this step, the optimal angle data under the minimum pulsation needs to be obtained by simulation. After collecting data, select and classify the data, and then train and improve the neural network, That nonlinear relationship is introduced into the motor control strategy, so that the motor can automatically adjust its opening angle according to different rotational speeds and load torques and achieve the purpose of reducing torque ripple under different working conditions. Finally, the motor simulation model is built in Matlab/Simulink, and the results are analyzed. This control strategy can control and reduce torque ripple.