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The high-frequency square-wave injection approach is frequently utilized for rotor position estimation in IPMSMs operating without position sensors, particularly under low or zero speed conditions. To mitigate torque ripple induced by high-frequency excitation, this work develops an adaptive scheme that continuously adjusts injection amplitude in closed-loop systems via a Recurrent Neural Network. In this method, the RNN adjusts the injection amplitude based on d-axis current data and position error details, ensuring accurate position extraction while effectively reducing current harmonics and torque ripple. Simulation results demonstrate that compared to traditional fixed-amplitude injection methods, while ensuring the position tracking accuracy, the peak-to-peak value of torque ripple and the THD drop of current are reduced by 24.93% and 46.24% respectively.

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Research on Torque Ripple Suppression Based on RNN Neural Network Amplitude Optimization of Pulsating Injection

  • 会议论文

  • 发表日期：

    2026年

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  • Zhuoming Yu,
  • Zhen Chen,
  • Xuefei Mao

摘要

The high-frequency square-wave injection approach is frequently utilized for rotor position estimation in IPMSMs operating without position sensors, particularly under low or zero speed conditions. To mitigate torque ripple induced by high-frequency excitation, this work develops an adaptive scheme that continuously adjusts injection amplitude in closed-loop systems via a Recurrent Neural Network. In this method, the RNN adjusts the injection amplitude based on d-axis current data and position error details, ensuring accurate position extraction while effectively reducing current harmonics and torque ripple. Simulation results demonstrate that compared to traditional fixed-amplitude injection methods, while ensuring the position tracking accuracy, the peak-to-peak value of torque ripple and the THD drop of current are reduced by 24.93% and 46.24% respectively.