Adaptive Generalized Complex Vector Observer for Sensorless Control of PMSM With Robustness to Current Measurement Errors

Model-based sensorless control techniques are popular in achieving high-performance control of permanent magnet synchronous motor (PMSM). However, low-order harmonics caused by inevitable current measurement errors are always present in the estimated back electromotive force (BEMF), which increases the position estimation error. In this article, an adaptive generalized complex vector observer (AGCVO) is proposed to accurately estimate the BEMF. First, an adaptive complex vector model is inversely designed based on the transfer function of a multiblock filter to eliminate the fundamental negative-sequence harmonic and dc offset error of the estimated BEMF. Then, the accurate extraction of the fundamental positive-sequence component is achieved by speed adaptive compensation, avoiding additional position compensation. Compared with the existing observers, the proposed observer effectively improves the accuracy of rotor position estimation by eliminating the low-order harmonics in the BEMF. Finally, the effectiveness of the proposed AGCVO is validated on an experimental platform.