Artificial Neural Network Control of a Magnetic Levitation System | Application of Artificial Neural Network in Magnetic Levitation System Control

Mustapha Muhammad

ISBN 10: 3659149020 / ISBN 13: 9783659149023

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Artificial Neural Network Control of a Magnetic Levitation System | Application of Artificial Neural Network in Magnetic Levitation System Control | Mustapha Muhammad | Taschenbuch | 112 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659149023 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 106423231

Bibliographic Details

• Title: Artificial Neural Network Control of a Magnetic Levitation System | Application of Artificial Neural Network in Magnetic Levitation System Control
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659149020
• ISBN 13: 9783659149023
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 185 grams
• Seller catalogs:
  • Bücher

About this title

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.

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