"Experimental data for E-ASHA manuscript"

"Current sensor inaccuracies and system delays (collectively termed hybrid errors) degrade the performance of finite control set model predictive current control (FCS-MPCC) in surface-mounted permanent magnet synchronous motor (SPMSM) drives, leading to increased torque ripple, speed fluctuations, and reduced efficiency. This paper proposes an enhanced adaptive selective harmonic annulation (E-ASHA) algorithm integrated within the FCS-MPCC framework to simultaneously compensate for both sensor-induced errors and delay-induced harmonics. The proposed method employs an LMS-based adaptive learning mechanism to selectively estimate and cancel targeted harmonic components in both q-axis and d-axis currents without requiring additional sensors, motor parameter identification, or observer-based structures. By exploiting the coupling between dq-axis currents and motor speed dynamics, the strategy effectively mitigates torque and speed ripple while preserving the predictive nature of FCS-MPCC. Experimental validation on a 1-kW SPMSM platform demonstrates up to 80.5% reduction in current total harmonic distortion at low speed and more than 46% torque ripple suppression under high-speed heavy-load conditions while maintaining computational efficiency."