Improved Position Observer Using Adaptive Training Control-Based Filter for Interior Permanent Magnet Synchronous Motor Drives

Abstract

Rotor position is the key information to achieve superior performance for sensorless control of interior permanent magnet synchronous motor (IPMSM). Nevertheless, the back electromotive force (EMF) model-based position estimation suffers from severe contamination from the fifth and seventh harmonics resulting from inverter nonlinearity and flux spatial harmonics. Therefore, an adaptive training control-based adaptive filter combined with a sliding-mode observer (SMO) is presented for harmonics rejection in the estimated back-EMF, thus improving the rotor position estimation performance. This method, based on the steepest descent algorithm, is capable of self-adjusting harmonic coefficients to obtain the fundamental component online under various frequency conditions adaptively. Additionally, the proposed method has a simpler structure and less calculation burden since its reference signal is self-generated without external injection compared to the conventional method. The effectiveness is verified by experiments at a 1.5-kW IPMSM drive platform.