Abstract:
Suspension is a very important part of the functionality of a car. It supports
the comfort of the passengers and can be used to improve the car's stability. If a
magnetorheological (MR) damper is used, the damping performance can be
adjusted according to the road condition. In this research, an attempt to model an
MR damper using multiple artificial neural networks (ANNs) was conducted. The
measurement data was obtained by using a damper dynamometer. Eight
measurements were conducted to cover the coil current range between 0 and 1,400
mA. The MR damper model was then integrated into the semi-active suspension of a
quarter-car model. Furthermore, the fuzzy logic controller (FLC) design for the
semi-active suspension system was conducted. This artificial intelligence control
deployed a novel approach in using the frequency content of the road surface as the
input. The control scheme was tested by giving a sinusoidal bumpy road and a
pseudo-random bumpy road as the input to the quarter-car model. The FLC
performed well and was able to reduce the transmitted excitation from the road to
the car chassis.
