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Magnetic levitation (maglev) systems are nowadays employed in applications ranging from non-contact bearings and vibration isolations of sensitive machinery to high-speed passenger trains. In this work a mathematical model of a laboratory maglev system was derived using Lagrangian approach. Three controllers were designed for the maglev system and their performances were investigated via simulation using Simulink. Firstly, a pole-placement controller was designed on the basis of specifications on peak overshoot and settling time. Secondly, a nonlinear state feedback linearization control scheme was designed. Finally 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. The robustness of the three control schemes was investigated with respect to parameter variations and reference step input magnitude variations. Presented Simulink simulation results show that the feedback linearization control scheme performed best followed by linear state feedback pole-assignment control.
About the author
Mustapha Muhammad was born in Kano, Nigeria on the 24th July 1977. He received the Bachelor of Engineering and the Master of Engineering degrees from Bayero University, Kano, Nigeria in January 2001 and February 2007 respectively. In March 2004, he joined Department of Electrical Engineering, Bayero University, Kano, Nigeria as a graduate assistant
