An Application of the Cerebellar Model Articulation Controller for a Switched Reluctance Rotor Position Estimator

Abstract

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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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1992
Accession Number
ADA271769

Entities

People

  • Jenifer M. Shannon

Organizations

  • Naval Air Warfare Center Aircraft Divison

Tags

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  • Advanced Electronics
  • Air Platforms
  • Energy and Power Technologies

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  • Aircrafts
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  • Estimators
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  • Magnetic Materials
  • Metal Oxide Semiconductors
  • Modules (Electronics)
  • Neural Networks
  • Power Electronics
  • Semiconductors

Readers

  • Aerodynamics.
  • Electrical Engineering
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks