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

Energies

卷:14

Krzysztof Łuksza¹  Voitech Stankevič²  Justas Dilys² 

[1] Department of Power Electronics and Electrical Machines, Gdansk University of Technology, ul. Sobieskiego 7, 80-216 Gdansk, Poland;

[2] State Research Institute Center for Physical Sciences and Technology, Sauletekio Ave. 3, LT-10257 Vilnius, Lithuania;

关键词: PMSM;    sensorless;    EKF;    ARM;    fast execution;   

DOI  :  10.3390/en14123491

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown