Neural Network Control of a Laboratory Magnetic Levitator

Abstract

Magnetic levitation (maglev) systems are nowadays employed in applications ranging from non-contact bearings and vibration isolation of sensitive machinery to high-speed passenger trains. In this chapter a mathematical model of a laboratory maglev system was derived using the Lagrangian approach. A linear pole-placement controller was designed on the basis of specifications on peak overshoot and settling time. A 3-layer feed-forward Artificial Neural Network (ANN) controller comprising 3-input nodes, a 5-neuron hidden layer, and 1-neuron output layer was trained using the linear state feedback controller with a random reference signal. Simulations to investigate the robustness of the ANN control scheme with respect to parameter variations, reference step input magnitude variations, and sinusoidal input tracking were carried out using SIMULINK. The obtained simulation results show that the ANN controller is robust with respect to good positioning accuracy.