Speed-sensorless Predictive Current Controlled PMSM Drive With Adaptive Filtering-based MRAS Speed Estimators

In this study, the stator currents-based model reference adaptive system (MRAS) estimators, which uses adaptive filtering algorithms, called the least mean squares (LMS) and least mean Kurtosis (LMK) algorithms, in the adaptation mechanism, are proposed for speed estimation of the PMSM. The proposed MRAS estimators have been directly estimated the rotor speed of the PMSM by taking into account the error between the measured stator currents called the reference model and the stator currents at the output of the adaptive model. The performance of proposed estimators and the finite control set model predictive current control (FCS-MPCC) based speed/position sensorless PMSM driver are tested and verified in simulations. In addition, the proposed estimators are compared to a fixed-gain PI controller-based MRAS. Simulation results and mean square errors (MSEs) obtained from both LMS and LMK-based MRAS algorithms demonstrated that the proposed estimators have higher performance compared to MRAS using a traditional PI controller. Moreover, the proposed estimators have eliminated the need for a fixed-gain PI controller, which is often used in MRAS structures.

Automated summary

This study proposes stator currents-based model reference adaptive system (MRAS) estimators for speed estimation of the PMSM. Adaptive filtering algorithms, namely the least mean squares (LMS) and least mean Kurtosis (LMK) algorithms, are used in the adaptation mechanism. The proposed estimators directly estimate the rotor speed of the PMSM by considering the error between the measured stator currents (reference model) and the stator currents at the output of the adaptive model. Simulation results demonstrate that the proposed estimators outperform MRAS using a traditional PI controller and eliminate the need for a fixed-gain PI controller often used in MRAS structures.