Authors:
Juan C. Tudón-Martínez
;
Ruben Morales-Menendez
;
Ricardo A. Ramirez-Mendoza
and
Luis E. Garza-Castañón
Affiliation:
Tecnolégico de Monterrey, Mexico
Keyword(s):
Magneto-Rheological Damper, Artificial Neural Networks, Semiactive Suspension Control.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
A model for a Magneto-Rheological (MR) damper based on Artifical Neural Networks (ANN) is proposed. The ANN model does not require regressors in the input and output vector, i.e. is considered static. Only one sensor is used to achieve a reliable MR damper model which is compared with experimental data provided from two MR dampers with different properties. The RMS of the error is used to measure the model accuracy; from both MR dampers, an average value of 7.1% of total error in the force signal is obtained by taking into account 5 different experiments. The ANN model, which represents the nonlinear behavior of an MR damper, is used in a suspension control system of a Quarter of Vehicle (QoV) in order to evaluate the comfort of passengers maintaining the road holding. A control technique with the MR damper model is compared with a passive suspension system. Simulation results show the effectiveness of a semiactive suspension versus the passive one. The RMS of the comfort signal impr
oves 7.4% with the MR damper while the road holding gain in the frequency response shows that the safety in the vehicle can be increased until 40.4% with the semiactive suspension system. The accurate MR damper model validates a realistic QoV response compliance.
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