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<p>This paper proposes an optimized predictive torque control (PTC) strategy for induction motors (IMs) based on automatic tuning of weighting factors (WFs) using the rooted tree optimization (RTO) algorithm. In conventional PTC, WFs are selected by trial-and-error, which is time-consuming and does not ensure optimal performance. Moreover, reducing torque ripple often increases stator flux ripple and average switching frequency (ASF), leading to higher losses. In order to solve this problem, the tuning of WFs is formulated as an optimization problem aiming to minimize torque ripple, stator flux ripple, and ASF simultaneously. The major contribution of this work is the application of the RTO algorithm to automatically determine optimal WFs, ensuring a balanced trade-off between these conflicting objectives without manual intervention. Simulation results under MATLAB/Simulink environment demonstrate the effectiveness of the proposed method compared to conventional tuning strategies with reduced torque and stator flux ripples and optimized ASF.</p>

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Optimized Predictive Torque Control of Induction Motor for Reduced Torque Ripple and Average Switching Frequency based on Rooted Tree Optimization

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Youcef Bekakra,
  • Mabrouka Romdhane,
  • Abdelmalek Gacem,
  • Mohamed Naoui,
  • Mostefa Kermadi,
  • Om P. Malik,
  • Saad Mekhilef

摘要

This paper proposes an optimized predictive torque control (PTC) strategy for induction motors (IMs) based on automatic tuning of weighting factors (WFs) using the rooted tree optimization (RTO) algorithm. In conventional PTC, WFs are selected by trial-and-error, which is time-consuming and does not ensure optimal performance. Moreover, reducing torque ripple often increases stator flux ripple and average switching frequency (ASF), leading to higher losses. In order to solve this problem, the tuning of WFs is formulated as an optimization problem aiming to minimize torque ripple, stator flux ripple, and ASF simultaneously. The major contribution of this work is the application of the RTO algorithm to automatically determine optimal WFs, ensuring a balanced trade-off between these conflicting objectives without manual intervention. Simulation results under MATLAB/Simulink environment demonstrate the effectiveness of the proposed method compared to conventional tuning strategies with reduced torque and stator flux ripples and optimized ASF.