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<p>Direct torque control (DTC) is widely acknowledged as an effective approach for regulating the electromagnetic torque of induction motors (IM). However, the variable switching frequency of conventional DTC results in significant flux and torque ripples and degraded low-speed performance. To address these limitations, this work presents an improved DTC strategy based on artificial neural network (ANN) with a flux-oriented structure for voltage-source inverters. The proposed method replaces the conventional hysteresis comparators and switching table, thereby enabling adaptive regulation of stator flux and electromagnetic torque. Magnetic saturation effects are incorporated to improve performance under nonlinear operating conditions. Simulation and experimental results using a TMS320F28379D DSP platform validate the effectiveness and practical feasibility of the proposed approach.</p>

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Artificial Neural Network–Enhanced Direct Torque Control for Induction Motors Under Magnetic Saturation Effects

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Nguyen Vinh Quan,
  • Mai Thang Long

摘要

Direct torque control (DTC) is widely acknowledged as an effective approach for regulating the electromagnetic torque of induction motors (IM). However, the variable switching frequency of conventional DTC results in significant flux and torque ripples and degraded low-speed performance. To address these limitations, this work presents an improved DTC strategy based on artificial neural network (ANN) with a flux-oriented structure for voltage-source inverters. The proposed method replaces the conventional hysteresis comparators and switching table, thereby enabling adaptive regulation of stator flux and electromagnetic torque. Magnetic saturation effects are incorporated to improve performance under nonlinear operating conditions. Simulation and experimental results using a TMS320F28379D DSP platform validate the effectiveness and practical feasibility of the proposed approach.