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<p>Achieving efficient and precise control of induction motors (IMs) is essential for industrial applications. However, conventional IM control methods depend on current sensors, which add hardware complexity, increase costs, and compromise reliability due to noise and sensor faults. This research introduces self-learning model-based control (SLMBC), a novel sensorless control strategy that employs a three-layer backpropagation neural network to estimate three-phase currents in real-time, eliminating the need for direct current measurements. The SLMBC strategy incorporates robust control techniques to enhance system stability, ensuring fast response and accurate tracking. Additionally, a three-level inverter with phase opposition disposition modulation is implemented to optimize voltage regulation, mitigate common mode voltage, and suppress harmonics, thereby improving output quality. The proposed method is validated through comprehensive simulations and real-time experiments on the OP4510 platform, demonstrating superior dynamic response, high control precision, and strong noise immunity across various operating conditions. These results establish SLMBC as a reliable, cost-effective alternative to conventional sensor-based IM control, offering enhanced adaptability and robustness for industrial motor drive applications.</p>

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Self-learning model-based control for sensorless induction motor drives

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Nguyen Quan Vinh,
  • Thanh-Lam Le

摘要

Achieving efficient and precise control of induction motors (IMs) is essential for industrial applications. However, conventional IM control methods depend on current sensors, which add hardware complexity, increase costs, and compromise reliability due to noise and sensor faults. This research introduces self-learning model-based control (SLMBC), a novel sensorless control strategy that employs a three-layer backpropagation neural network to estimate three-phase currents in real-time, eliminating the need for direct current measurements. The SLMBC strategy incorporates robust control techniques to enhance system stability, ensuring fast response and accurate tracking. Additionally, a three-level inverter with phase opposition disposition modulation is implemented to optimize voltage regulation, mitigate common mode voltage, and suppress harmonics, thereby improving output quality. The proposed method is validated through comprehensive simulations and real-time experiments on the OP4510 platform, demonstrating superior dynamic response, high control precision, and strong noise immunity across various operating conditions. These results establish SLMBC as a reliable, cost-effective alternative to conventional sensor-based IM control, offering enhanced adaptability and robustness for industrial motor drive applications.