Delay‐Compensated Adaptive Vector Filter Phase‐Locked Loop Integrated With <scp>GRA</scp> ‐Based Model Predictive Torque Control for Sensorless <scp>IPMSM</scp> Drives

ABSTRACT Increasing system reliability and reducing costs for electric vehicle (EV) applications requires reducing sensor dependency in permanent magnet synchronous motors (IPMSMs). Therefore, sensorless operation remains a significant challenge, particularly over a wide speed range. Adaptive vector filters (AVF) and phase‐locked loops (PLL) are integrated with a grey relational analysis (GRA)‐based model predictive torque control (MPTC) strategy for IPMSMs in the proposed sensorless control framework. The proposed method incorporates an amplitude and phase offset to counteract feedback delay‐induced amplitude and phase distortions in the back electromotive force (BEMF) filtered by the AVF. The ideal PLL is implemented using a differential calculation structure that preserves closed‐loop dynamics. In comparison, the steady‐state position estimation error is effectively eliminated using an open‐loop deviation compensator. Incorporating GRA‐based optimization enables online adaptation of the weighting factors, achieving an effective trade‐off among torque ripple, flux distortion, and switching frequency. An experimental validation of the proposed algorithm is carried out on a Speedgoat real‐time hardware‐in‐the‐loop (HIL) platform. The obtained results confirm that the method significantly improves sensorless estimation accuracy, torque response, and robustness against parameter variations, demonstrating its suitability for rail transit applications.