Comparison of full-model and reduced-model EKF based position and speed estimators for sensorless DTC of permanent magnet synchronous machines

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.

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Comparison of full-model and reduced-model EKF based position and speed estimators for sensorless DTC of permanent magnet synchronous machines

Cited by 3 publications

References 6 publications

An improved sensorless DTC-SVM for three-level inverter-fed permanent magnet synchronous motor drive

An improved sensorless DTC-SVM for three-level inverter-fed permanent magnet synchronous motor drive

Reconfigurable filter structure designed for different positioning systems

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

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