Usage of Magnetorheological Damper in Active Front Bumper System for Frontal Impact Protection

Abstract

In this paper, the effectiveness of the active bumper system to reduce the jerk of a vehicle during collision is discussed. The mathematical model is done by using MATLAB 7.0 to simulate a collision between a pendulum and a vehicle installed with the active bumper system. In the active bumper system, it consists of three parts which are magnetorheological(MR) model, inner controller and outer loop controller. The validated model is used to develop an inner loop controller by implementing a close-loop PI control to track the desired damping force through simulation. The governing equations of motions of vehicle collision and MR damper model are then integrated with the well known control strategy namely skyhook control. The performance of skyhook control is then compared with the vehicle with passive damper and common vehicle by using computer simulation in order to reduce the acceleration and the jerk of the vehicle during collision.

Vehicle body pitch control through integration of MR damping system implemented in both vehicle suspension and front-end structure

“…Where the mentioned comparison showed noticeable improvements on vehicle acceleration and jerk. 5 Furthermore, the efficiency and controllability of using a novel adaptive energy absorber using A Magnetorheological Elastomer (MRE) material as a replacement for the passive bumper systems were discussed by Sun et al 6 Some researchers suggested that the MR damper can be placed at the bottom-most portion of the frontal fork arrangement while, the other end of the fork is connected to the bonnet of the vehicle on the upper fork portion. Hence, that arrangement showed a reduction in the collision effect by replacing the conventional bumpers with MR bumpers as mentioned by Ahamed et al 7 Considering vehicle dynamics control systems (VDCS), a 6 DOF mathematical vehicle model was developed to study the effect of VDCS on high-speed vehicle crashes.…”

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

“…In addition to parametric modelling, there are two non-parametric approaches that have been used in the development of MRF damper models: mathematical functions and the computational intelligence paradigms. The mathematical functions approach was used to obtain accurate hysteresis behaviour, such as hyperbolic tangent (Baxter and Caicedo, 2014;Jiang and Christenson, 2012;Tian et al, 2015), polynomial (Choi et al, 2001;Pokaad et al, 2011Pokaad et al, , 2013Ubaidillah et al, 2011), and sigmoid function (Liao and Wang, 2003;Ma et al, 2006), but its accuracy in predicting MRF damper behaviour yielded a relatively large error compared to experimental characterisation data. On the other hand, the second approach of computational intelligence paradigms using neural networks (Khalid et al, 2014;Wang and Liao, 2005;Zeinali et al, 2014), adaptive neuro-fuzzy inference systems (ANFIS) Imaduddin et al (2017); Rahmat et al (2019a, b); Zeinali et al (2013), and genetic algorithms (Giuclea et al, 2004) has been investigated by many researchers.…”