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Neural Network Emulation of a Magnetically Suspended Rotor

[+] Author Affiliations
A. Escalante, V. Guzmán, M. Parada, L. Medina, S. E. Diaz

Universidad Simón Bolívar, Caracas, Venezuela

Paper No. GT2002-30294, pp. 615-625; 11 pages
doi:10.1115/GT2002-30294
From:
  • ASME Turbo Expo 2002: Power for Land, Sea, and Air
  • Volume 4: Turbo Expo 2002, Parts A and B
  • Amsterdam, The Netherlands, June 3–6, 2002
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3609-6 | eISBN: 0-7918-3601-0
  • Copyright © 2002 by ASME

abstract

The use of magnetic bearings in high speed/low friction applications is increasing in industry. Magnetic bearings are sophisticated electromechanical systems, and modeling magnetic bearings using standard techniques is complex and time consuming. In this work a Neural network is designed and trained to emulate the operation of a complete system (magnetic bearing, PID controller and power amplifiers). The neural network is simulated and integrated into a virtual instrument that will be used in the laboratory both as a teaching and a research tool. The main aims in this work are: 1-Determining the minimum amount of artificial neurons required in the neural network to emulate the magnetic bearing system. 2-Determining the more appropriate ANN training method for this application. 3-Determining the errors produced when a neural network trained to emulate system operation with a balanced rotor is used to predict system response when operating with an unbalanced rotor. The neural network is trained using as input the position data from the proximity sensors; neural network outputs are the control signals to the coil amplifiers.

Copyright © 2002 by ASME

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