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Reducing torque ripple for switched reluctance motors by current reshaping neural network
Benqin Jing1, Guofu Liang2, Xuanju Dang3
1College of Artificial Intelligence, Guilin University of Aerospace Technology, Guilin, 541004, China.
Scientific Reports
|May 20, 2025
Summary
This study introduces a current reshaping neural network (CRNN) to significantly reduce torque ripple in switched reluctance motors (SRMs). The CRNN optimizes current profiles for smoother motor operation.
Area of Science:
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background:
- Switched reluctance motors (SRMs) are widely used but suffer from high torque ripple.
- Torque ripple limits the performance and application range of SRMs.
Purpose of the Study:
- To propose and validate a novel Current Reshaping Neural Network (CRNN) for mitigating torque ripple in SRMs.
- To enhance the operational smoothness and efficiency of SRMs through precise current control.
Main Methods:
- Analysis of the relationship between current and electromagnetic torque using an indirect torque control method.
- Development of a CRNN to model current and generate precise phase currents.
- Implementation of CRNN with implicit function based on total current and rotor angle, adjusted via proportion differentiation compensation current.
Main Results:
- The CRNN effectively reduces torque ripple by modifying phase current profiling.
- Simulations and experiments on a three-phase 12/8 SRM demonstrated the method's efficacy.
- Consistent performance was observed across various operating conditions.
Conclusions:
- The proposed CRNN is a viable solution for reducing torque ripple in SRMs.
- This method improves SRM performance, enabling wider applications.
- The CRNN offers precise control over current waveforms for enhanced motor operation.
Keywords:
Current profilingCurrent reshapingIndirect torque controlNeural networkSwitched reluctance motorsMore Related Videos
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