Artificial Neural Network Control of a Magnetic Levitation System: Application of Artificial Neural Network in Magnetic Levitation System Control - Couverture souple

Muhammad, Mustapha

 
9783659149023: Artificial Neural Network Control of a Magnetic Levitation System: Application of Artificial Neural Network in Magnetic Levitation System Control
Couverture souple
ISBN 10 : 3659149020 ISBN 13 : 9783659149023
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

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.

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Présentation de l'éditeur

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.

Biographie de l'auteur

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

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659149020
ISBN 13 
9783659149023
Reliure
Broché
Nombre de pages
112
Coordonnées du fabricant
non disponible
Personne responsable
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