Implementation and tuning of the Extended Kalman Filter for a sensorless drive working with arbitrary stepper motors and cable lengths

In this paper the important practical issues of tuning and implementation of an Extended Kalman Filter for a sensorless hybrid stepper motor drive working with long cables is considered. A method to tune the filter using one set of data acquired from the real system is proposed. From this dataset, the system parameters and the Extended Kalman Filter's covariance matrices are estimated. The hardware and software implementation of the Extended Kalman Filter in the drive is also described, with specific emphasis on the code optimisation steps that are necessary to execute the filter at the desired sampling rate. Moreover, the developed drive's data acquisition capabilities and the experimental testbench used in the tuning and validation of the filter are discussed. Experimental results prove the effectiveness of the tuning method and implementation.

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