A method of estimating the rotor position of a switched reluctance machine without the need for a rotor-mounted position sensor has been developed. This method takes advantage of the information derived from known phase voltage and current waveforms. The information is fed as the inputs to a neural network, which after being trained, can correctly map the rotor position to its output. The most accurate mapping results were obtained using a Cerebellar Model Articulation Controller (CMAC) neural network. The performance of the neural network has been tested with measured waveforms from a three phase 120 HP switched reluctance motor. It successfully maps the rotor position with an average root mean square error of one tenth of a mechanical degree.


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    Title :

    A Switched Reluctance Machine Rotor Position Estimator: A Neural Network Application


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Aerospace Technology Conference and Exposition ; 1993



    Publication date :

    1993-09-01




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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