Sensorless direct torque control of PMSM with reduced model Extended Kalman filter

Abstract

The aim of this paper is to study the use of the Extended Kalman filer (EKF) for sensorless control of a permanent magnet synchronous motor (PMSM) drive controlled by direct torque control (DTC). In contrast to the vector control, the DTC allows for better compensation of the dead-time effects, however, it also needs to run with much shorter sampling period. The challenge for the EKF is to minimize its execution time. This is achieved by using state space model of the drive with only two state variables, the rotor speed and the rotor position, which will be known as the reduced model. We show in simulations and experiments, that the EKF with this reduced model has the same performance as the four-dimensional full order model at much lower computational cost. This allows to use the EKF even in DTC with very short sampling time and take full advantage of the dead-time compensation. Due to better reconstruction of the voltage vector, the sensorless DTC is able to operate the drive at lower speed than the vector control with the same estimator. All experiments were carried out on a laboratory prototype of the drive with rated power of 11 kW and 44 poles. Control algorithms using DTC with sampling periods of 30 or 50 µs and vector control with sampling period of 125 µs were compared. The sensorless DTC with the reduced model EKF was found to be equal to DTC with the full model EKF in terms of performace and superior in terms of computational cost. Both sensorless DTC algorithms outperformed the vector control algorithm in accuracy of the estimation especially at low speed.

Implementation of Extended Kalman Filter with Optimized Execution Time for Sensorless Control of a PMSM Using ARM Cortex-M3 Microcontroller

“…In order to address this problem, various optimization algorithms which can lower the computational costs have been reported. Computational cost of the full order EKF can be minimized by a reduced order model [5][6][7]. The idea of these filters is to reduce the number of states of the model by engineering approximation methods.…”