Robustness Improvement of Model-Based Sensorless SPMSM Drivers Based on an Adaptive Extended State Observer and an Enhanced Quadrature PLL

Conventional model-based sensorless surface-mounted permanent magnet synchronous motor (SPMSM) drivers suffer deteriorated performance in transient-state operation such as accelerating during wide-speed range or encountering large load torque disturbance. In this article, an effective sensorless control solution is proposed to improve the robustness of sensorless SPMSM drivers in transient-state operation. Two extended state observers (ESOs) are utilized to estimate the αβ-axis back electromotive forces (EMFs). An adaptive bandwidth tuning scheme is proposed to online tune the parameters of the ESOs, with which the desired estimation performance of back EMFs can be achieved in wide-speed operation range. Additionally, the dynamic mechanical behavior of the sensorless control system during large load torque transient is investigated by using small-signal analysis, and then a corresponding speed estimation compensation scheme for the conventional quadrature phase-locked loop (QPLL) is developed, with which the capability of QPLL in tracking the rapidly changing speed can be significantly enhanced. Experiments on an SPMSM demonstrate the superior robustness of the proposed method to that of the conventional method in transient-state operation.

I. Introduction

Sensorless surface-mounted permanent magnet synchronous motor (SPMSM) drive systems are increasingly used in household and industrial applications due to their high reliability and low cost [8]. The existing sensorless control techniques for SPMSMs can be classified into two categories: 1) high-frequency signal injection-based methods [1], [2], which rely on the magnetic saturation of SPMSM; and 2) model-based methods [3]–[29], which rely on the estimation of flux linkage or back electromotive force (EMF). The flux linkage estimation based methods have the advantage of wide-speed applicability because the flux linkage is independent of the operating speed [4], [5]. However, since the flux linkage is obtained by integrating the back EMF, the nonzero initial value will eventually result in an evident error to the estimated flux, thereby degrading the accuracy of the estimated position. Currently, in medium- and high-speed regions, methods based on back EMF estimation hold a dominant role. These methods usually include a back EMF estimator and a cascading position/speed extractor which extracts the rotor speed and position from the estimated back EMF. Various methods can be utilized to estimate the back EMF, such as extended Kalman filter [3], sliding-mode observer (SMO) [8]–[12], disturbance observer [5], [7], extended state observer (ESO) [13]–[15], etc. [17]–[29].