A Position-Feedback-Free and Inverter-Nonlinearity-Immune Inductance Estimation Method for PMSM Drives Without Signal Injection

Accurate online maps of the incremental inductances <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L_d</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L_q</i> are critical to model-based drives, sensorless observers, and real-time fault diagnosis. Existing techniques either rely on slow, equipment-intensive offline tests or inject auxiliary signals that disturb the drive, depend on precise rotor position, and are difficult to calibrate against inverter nonlinearities. This paper sidesteps those limitations by recasting the fundamental voltage vector sequence as a differential virtual-voltage-vector that inherently rejects dead-time distortion and device voltage drops. A piece-wise multisample linear regression—applied only to the quasi-linear segment of each current trace—then yields a dead-time-immune, noise-resilient estimate of current slopes. The proposed approach introduces three mutually reinforcing advances: (i) a virtual-voltage-vector-oriented framework that inherently accounts for inverter non-linearities; (ii) a multi-sampling-based current-slope estimation strategy that is immune to dead-time effects and suppresses noise; and (iii) a signal-injection-free, rotor-position-independent inductance observer that is explicitly built on the first two advances, harnessing their accurate voltage modeling and derivative measurements. The proposed estimator is evaluated against the standard AC-standstill test and a current-slope online method, the mean error remains below 5% in every case. Validation spans the full operating range, including extreme load and speed transients.