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Shock and Vibration
Volume 20, Issue 1, Pages 171-179

Modeling and Identification of Electromagnetic Actuator for the Control of Rotating Machinery

T.S. Morais,1 J. Der Hagopian,2 V. Steffen Jr.,1 and J. Mahfoud2

1Federal University of Uberlândia, School of Mechanical Engineering, Campus Santa Monica, Uberlândia, Brazil
2Université de Lyon, Laboratoire de Mécanique des Contacts et des Structures, Institut National des Sciences Appliquées de Lyon, Lyon, France

Received 11 November 2011; Revised 15 May 2012

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work deals with the design and the assessment of electromagnetic actuators (EMAs) for the control of rotating machines. The system studied has a hybrid bearing that exhibits nonlinear behavior. The system is composed of a horizontal flexible shaft supported by two ball bearings at one end and a roller bearing that is located in a squirrel cage at the other end. Four identical EMAs supplied with constant current are utilized. The EMAs associated to the squirrel cage constitutes the hybrid bearing. The aim is to develop a strategy in order to define and to identify a reliable model necessary for the control of rotating machinery in the presence of localized non-linearity. The identification strategy consists in modeling the system with as many sub-models as needed that are identified separately. This enables obtaining a straightforward modeling of rotating machinery even in the case in which system components are frequency or time dependent. For the system studied, two sub-models were necessary. First the EMAs were modeled by using classical equations of electromagnetism and then identified experimentally. Then, a linear model of the shaft mounted on its bearings was defined by using the finite element method and was identified successfully. The model of the system was adjusted after assembling the different identified sub-models. The identification is carried out by using a pseudo-random search algorithm. The model of the system is then assessed for different configurations. The results obtained demonstrate the effectiveness of the developed strategy.