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<p>This paper addresses the high-frequency oscillations issue commonly associated with the sliding mode observer (SMO) algorithm in sensorless control of surface-mounted permanent magnet synchronous motor (SPMSM) and the problem of parameter variation caused by magnetic saturation and temperature rise during motor operation. A novel information acquisition optimized recursive least squares (IAORLS) algorithm, which incorporates an IAO optimized forgetting factor, is proposed to identify motor parameters. The identified parameters are utilized in conjunction with the Super Twisting Algorithm Sliding Mode Observer (STA-SMO) for motor control. By discretizing the stator voltage equations of the SPMSM via the forward Euler method, IAORLS enables efficient parameter identification. These parameters are then integrated into the STA-SMO, allowing the observer to receive real-time updates, thereby preventing performance degradation due to parameter mismatches. Additionally, the method reduces high-frequency oscillations when the motor’s extended back electromotive force (back-EMF) is observed, improving the accuracy of rotor position and speed estimation. Simulations and experimental results confirm the effectiveness and reliability of the proposed approach.</p>

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PMSM sensorless control based on super-twisting algorithm sliding mode observer with the IAORLS parameter estimations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Hui Zhang,
  • Peng Ran,
  • Zehua Zhang

摘要

This paper addresses the high-frequency oscillations issue commonly associated with the sliding mode observer (SMO) algorithm in sensorless control of surface-mounted permanent magnet synchronous motor (SPMSM) and the problem of parameter variation caused by magnetic saturation and temperature rise during motor operation. A novel information acquisition optimized recursive least squares (IAORLS) algorithm, which incorporates an IAO optimized forgetting factor, is proposed to identify motor parameters. The identified parameters are utilized in conjunction with the Super Twisting Algorithm Sliding Mode Observer (STA-SMO) for motor control. By discretizing the stator voltage equations of the SPMSM via the forward Euler method, IAORLS enables efficient parameter identification. These parameters are then integrated into the STA-SMO, allowing the observer to receive real-time updates, thereby preventing performance degradation due to parameter mismatches. Additionally, the method reduces high-frequency oscillations when the motor’s extended back electromotive force (back-EMF) is observed, improving the accuracy of rotor position and speed estimation. Simulations and experimental results confirm the effectiveness and reliability of the proposed approach.