Abstract
The control of a high performance alternative current (AC) motor drive under sensorless operation needs the accurate estimation of rotor position. In this paper, one method of accurately estimating rotor position by using both motor complex number model based position estimation and position estimation error suppression proportion integral (PI) controller is proposed for the sensorless control of the surface permanent magnet synchronous motor (SPMSM). In order to guarantee the accuracy of rotor position estimation in the flux-weakening region, one scheme of identifying the permanent magnet flux of SPMSM by extended Kalman filter (EKF) is also proposed, which formed the effective combination method to realize the sensorless control of SPMSM with high accuracy. The simulation results demonstrated the validity and feasibility of the proposed position/speed estimation system.
Highlights
Permanent magnet synchronous motor (PMSM) has been widely used in many automation control fields due to its advantages of superior power density, maintenance-free operation, and high controllability [1,2,3]
The rotor position angle is estimated by a novel position estimator based on complex number model of surface permanent magnet synchronous motor (SPMSM)
By using an error suppression proportion integral (PI) controller that is combined with imaginary part of estimator, the estimation error of rotor position angle caused by the uncertainty of system parameters is converged to zero quickly
Summary
Permanent magnet synchronous motor (PMSM) has been widely used in many automation control fields due to its advantages of superior power density, maintenance-free operation, and high controllability [1,2,3]. The primary methods for position estimation under the sensorless condition can be divided into several main categories: adaptive approaches, back-electromagnetic force (back-EMF) based methods, reduced order observer methods, EKF based methods, and signal injection methods for low speed range [3,4,5,6,7,8,9,10] In these studies, the estimation result is very dependent on the accuracy of system parameters that include the motor parameters like inductance, resistance, and magnetic flux and the unmodeled parameters like inverter dead-time effect and inductance cross-saturation. In order to eliminate the influence of system uncertainty that affects the control result, the artificial intelligent techniques such as ANN were applied to estimate the position and speed of sensorless AC motor [18, 19] These methods can increase the robustness of the position and speed estimation through making a nonlinear function between the input and output of the control system.
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