Neural Network Emulation of a Magnetically Suspended Rotor

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, and (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.