Magnetorheological damper voltage control using artificial neural network for optimum vehicle ride comfort

  • M. F. Yakhni Mechanical Engineering Department, Faculty of Engineering, Beirut Arab University, Beirut, Lebanon Phone: +9613784466
  • M. N. Ali Mechanical Engineering Department, Faculty of Engineering, Beirut Arab University, Beirut, Lebanon Phone: +9613784466
  • M. A. El-Gohary Mechanical Engineering Department, Faculty of Engineering, Beirut Arab University, Beirut, Lebanon Phone: +9613784466
Keywords: Ride comfort, Full vehicle model, eight DOF model, MR damper, ANN

Abstract

Suspension system design is an important challenging duty that facing car manufacturers, so the challenge has become to design the best system in terms of providing ride comfort and handling ability under all driving situations. The goal of this paper is to provide assistance in enhancing the effectiveness of the suspension system. A full car model with eight degrees of freedom (DOF) was developed using MATLAB/Simulink. Validation of the Simulink model was obtained. The model was assumed to travel over a speed hump that has a half sine wave shape and amplitude that changing from 0.01 to 0.2 m. The vehicle was moving with variable speeds from 20 to 120 km/h. Magneto Rheological (MR) damper was implanted to the model to study its effect on ride comfort. Artificial Neural Network (ANN) was used to find the optimum voltage value applied to the MR damper, to skip the hump at least displacement. This network uses road profile and the vehicle speed as inputs. A comparison of the results for passive suspension system and model with MR damper, are illustrated. Results show that the MR damper give significant improvements of the vehicle ride performance over the passive suspension system.

Published
2021-03-08
How to Cite
Yakhni, M., Ali, M. N., & El-Gohary, M. A. (2021). Magnetorheological damper voltage control using artificial neural network for optimum vehicle ride comfort. Journal of Mechanical Engineering and Sciences, 15(1), 7648 - 7661. https://doi.org/10.15282/jmes.15.1.2021.03.0603
Section
Article