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

Abstract

This paper addresses the implementation and optimization of an Extended Kalman Filter (EKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex-M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of EKF estimator was reduced from 260.4 μs to 37.7 μs without loss of accuracy. To further reduce EKF execution time, the separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update, a novel method was proposed, and the performance of it an EKF estimator with separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update was analyzed. Simulation and experiments results validate that the proposed technique could provide the same accuracy with less computation time. A tendency of minimum Kalman gain and covariance matrices calculation frequency from rotor electrical frequency was analyzed and are presented in the paper.

Permanent Magnet Synchronous Motor Model Extension for High-Frequency Signal Injection-Based Sensorless Magnet Polarity Detection

“…Position-sensorless control methods for PMSMs determine the rotor position in terms of electrical angle without a mechanical shaft sensor usually based on phase current measurements and a suitable machine model. Sensorless control techniques that rely on the fundamental excitation model are capable of providing high-performance control above about 3% of the nominal speed when the back electromotive force (back-EMF) is sufficiently large [11][12][13][14][15]. To extend the sensorless operation range towards zero speed, saliency-based methods have been proposed that rely on inductance variation due to geometrical and saturation effects [16,17].…”

Saliency Model Extension for Sensorless Initial Position and Polarity Detection of Permanent Magnet Synchronous Motors

“…Sensorless methods for PMSMs determine the rotor position in terms of electrical angle without a mechanical position sensor usually based on phase current measurements and a suitable machine model. Sensorless control techniques that rely on the fundamental excitation model are capable of providing high-performance control above about 3 % of the nominal speed when the back electromotive force (back-EMF) is sufficiently large [10]- [16].…”

“…Figs. 16 and 17 show those page diagonal elements where the second and third indices are identical, but the first one is different. Similarly to the main diagonal elements, they have zero average values and a dominant first spatial harmonic in electrical angles.…”

State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning

“…The main drawbacks arising from the use of position sensors such as resolvers and encoders are the great noise vulnerability and the increasing cost and sizing of the drive, especially for low-power drives [2]. Consequently, many sensorless algorithms for PMSMs have been studied and proposed over the last few years in order to control the motor without any position sensor, offering significant advantages in terms of reduction of costs, increase of reliability, and removal of wires [3,4]. Sensorless control strategies can be mainly divided into two types: the active methods, using high-frequency signal injection to obtain the information of the rotor position, and the passive methods, consisting of model-based observers, which extract the rotor position by estimating the back electromotive force (back-EMF) or the rotor flux.…”

“…As the back-EMF is proportional to the rotor speed, the efficiency provided by these methods deteriorates at low speeds. For this reason, other common strategies are the Kalman filter-based observers proposed in [4,12], the linkage flux-based observer in [13], and the sliding mode-based observers studied in [14][15][16][17][18][19][20].…”