MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance

Juan C. Tudon-Martinez, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, Luis E. Garza-Castanón

Resultado de la investigación

1 Cita (Scopus)

Resumen

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 improves 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.
Idioma originalEnglish
Páginas493-502
Número de páginas10
EstadoPublished - 1 dic 2012
Publicado de forma externa
EventoIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence -
Duración: 1 dic 2012 → …

Conference

ConferenceIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Período1/12/12 → …

Huella dactilar

Compliance control
Neural networks
Sensors
Frequency response
Control systems

Citar esto

Tudon-Martinez, J. C., Morales-Menendez, R., Ramirez-Mendoza, R. A., & Garza-Castanón, L. E. (2012). MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance. 493-502. Papel presentado en IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence, .
Tudon-Martinez, Juan C. ; Morales-Menendez, Ruben ; Ramirez-Mendoza, Ricardo A. ; Garza-Castanón, Luis E. / MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance. Papel presentado en IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence, .10 p.
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title = "MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance",
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 improves 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.",
author = "Tudon-Martinez, {Juan C.} and Ruben Morales-Menendez and Ramirez-Mendoza, {Ricardo A.} and Garza-Castan{\'o}n, {Luis E.}",
year = "2012",
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pages = "493--502",
note = "IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence ; Conference date: 01-12-2012",

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Tudon-Martinez, JC, Morales-Menendez, R, Ramirez-Mendoza, RA & Garza-Castanón, LE 2012, 'MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance' Papel presentado en, 1/12/12, pp. 493-502.

MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance. / Tudon-Martinez, Juan C.; Morales-Menendez, Ruben; Ramirez-Mendoza, Ricardo A.; Garza-Castanón, Luis E.

2012. 493-502 Papel presentado en IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence, .

Resultado de la investigación

TY - CONF

T1 - MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance

AU - Tudon-Martinez, Juan C.

AU - Morales-Menendez, Ruben

AU - Ramirez-Mendoza, Ricardo A.

AU - Garza-Castanón, Luis E.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - 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 improves 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.

AB - 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 improves 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.

M3 - Paper

SP - 493

EP - 502

ER -

Tudon-Martinez JC, Morales-Menendez R, Ramirez-Mendoza RA, Garza-Castanón LE. MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance. 2012. Papel presentado en IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence, .