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.
A Switched Reluctance Machine Rotor Position Estimator: A Neural Network Application
Sae Technical Papers
Aerospace Technology Conference and Exposition ; 1993
1993-09-01
Conference paper
English
Outside-rotor switched reluctance machine for minimal hybrid vehicle application
Tema Archive | 2000
|Double-rotor switched reluctance machine design, simulations, and validations
IET | 2016
|Automotive engineering | 2014
|Evaluation of Rotor Conducting Screens on the Single-Phase Switched Reluctance Machine
Online Contents | 2006
|