Neural network emulation of a magnetically suspended rotor

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.