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.

This paper presents a newly designed switching linear feedback structure of sliding mode control (SLF-SMC) plugged with an model reference adaptive system (MRAS) based sensorless field-oriented control (SFOC) for induction motor (IM). Indeed, the performance of the MRAS depends mainly on the operating point and the parametric variation of the IM. Hence, the sliding mode control (SMC) could be considered a good control alternative due to its easy implementation and robustness. Simulation and experimentation results are presented to show the superiority of the proposed SLF-SMC technique in comparison with the classical PI controller under different speed ranges and inertia conditions.

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