Development of intelligent hybrid controller for torque ripple minimization in electric drive system with adaptive flux estimator: An experimental case study.

Minimizing torque ripple is still a major challenge in high‑performance electric drive systems because it lowers efficiency, raises acoustic noise, and speeds up mechanical wear. This project gives a clear, step‑by‑step guide for designing and testing an intelligent hybrid controller paired with an adaptive flux estimator for permanent‑magnet synchronous motor (PMSM) drives. The method mixes modern control ideas—such as fuzzy‑logic or neuro‑fuzzy systems—with classic vector control so the controller can suppress torque pulsations in real time. To keep performance steady when motor parameters shift or non‑linear effects appear, the adaptive flux estimator continuously tracks the stator flux linkages and the rotor position. Students will model the entire drive system in

MATLAB/Simulink, run comprehensive simulations, and validate the hybrid control algorithm. The evaluation plan lets them measure key metrics such as torque‑ripple factor, total harmonic distortion of the stator currents, and dynamic speed response under different load conditions. Following this framework helps students develop high‑efficiency motor‑drive controllers and provides a solid base for experimental testing and comparison with conventional proportional‑integral (PI) control schemes.

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