DocumentCode :
1873562
Title :
Neural network-based models for a vibration suppression system equipped with MR brake
Author :
Pawlus, Witold ; Karimi, Hamid Reza
Author_Institution :
Dept. of Eng., Univ. of Agder, Grimstad, Norway
fYear :
2012
fDate :
6-8 Sept. 2012
Firstpage :
330
Lastpage :
335
Abstract :
This paper is devoted to the modeling and simulation of a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system. The analysis of the Bouc-Wen and Dahl mathematical models of MR damper is presented. Influence of their parameters on the response is explored. Subsequently, by using the neural networks, the parameters characterizing each model are estimated. This makes it possible to perform the comparative analysis of the suggested damper models responses with the measured experimental results. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully utilized in the area of model-based control of semi-active vibration suppression systems.
Keywords :
artificial intelligence; magnetorheology; neural nets; vibration control; MR brake; MR damper; artificial intelligence; damper models; magnetorheological brake; mathematical models; neural network based models; semi active suspension system; vibration suppression system; Computational modeling; Magnetomechanical effects; Mathematical model; Neurons; Shock absorbers; Synthetic aperture sonar; Torque;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems (IS), 2012 6th IEEE International Conference
Conference_Location :
Sofia
Print_ISBN :
978-1-4673-2276-8
Type :
conf
DOI :
10.1109/IS.2012.6335156
Filename :
6335156
Link To Document :
بازگشت