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

Format:

Conference/Event

Author/Creator:

Shannon, Jenifer M., author.

Conference Name:

Aerospace Technology Conference & Exposition (1993-09-20 : Anaheim, California, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 1993

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

932560

Access Restriction:

Restricted for use by site license