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<p>The permanent magnet synchronous motor (PMSM) offers very competing innovations for traction drive in electric vehicles (EVs) due to its ameliorated efficiency and power density. Sparse damping of torsional vibration and torque ripple in PMSM can impact passenger comfort. This research introduces an enhanced control method for PMSMs that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a resonant (RES) control technique to tackle these challenges. The linear PI component has issues with nonlinearities and parameter fluctuations under high-dynamic situations; the fuzzy controller mainly handles these issues. However, the RES component is more appropriate for maintaining control accuracy over particular frequencies, where a single fuzzy might not be as reliable without a lot of tuning and extra rules. ANFIS is a highly appreciated method for dealing with uncertainties. The proposed ANFIS-based RES controller suppresses the undesirable torque ripples and associated noise in EVs and achieves precise reference tracking with disturbance repudiation. The execution of the proposed method for PMSM is analyzed for parametric variation and found to be robust. Simulation performed in MATLAB, additionally experimental validation using dSPACE 1104-based laboratory setup, confirms the robustness and effectiveness of the proposed method.</p>

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ANFIS-Based Resonant Controller for Mitigating Torque Ripples and Addressing Parametric Variation in PMSM-Driven Electric Vehicle

  • 期刊论文

  • 发表日期：

    2025年1月11日

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  • Shilpa Ranjan,
  • Madhusudan Singh,
  • Mini Sreejeth

摘要

The permanent magnet synchronous motor (PMSM) offers very competing innovations for traction drive in electric vehicles (EVs) due to its ameliorated efficiency and power density. Sparse damping of torsional vibration and torque ripple in PMSM can impact passenger comfort. This research introduces an enhanced control method for PMSMs that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a resonant (RES) control technique to tackle these challenges. The linear PI component has issues with nonlinearities and parameter fluctuations under high-dynamic situations; the fuzzy controller mainly handles these issues. However, the RES component is more appropriate for maintaining control accuracy over particular frequencies, where a single fuzzy might not be as reliable without a lot of tuning and extra rules. ANFIS is a highly appreciated method for dealing with uncertainties. The proposed ANFIS-based RES controller suppresses the undesirable torque ripples and associated noise in EVs and achieves precise reference tracking with disturbance repudiation. The execution of the proposed method for PMSM is analyzed for parametric variation and found to be robust. Simulation performed in MATLAB, additionally experimental validation using dSPACE 1104-based laboratory setup, confirms the robustness and effectiveness of the proposed method.