Abstract
Since magneto-rheological (MR) suspension has nonlinearity and uncertainty, a new adaptive fuzzy
control strategy using a hybrid Taguchi genetic algorithm (HTGA) is proposed to improve ride quality. The
controller consists of two control loops. The inner open loop controls a nonlinear MR damper to achieve
tracking of a desired force. The outer loop implements a fuzzy logic controller (FLC) using HTGA. The
HTGA is used to tune the membership functions and fuzzy control rules of FLC with initial skyhook
control rules. To verify the control performance, FLC based on HTGA for semi-active suspension system
is simulated. The simulation results show that FLC based on HTGA can achieve smaller acceleration root
mean square (RMS) than simple FLC and better ride quality compared with passive suspension under
random input.
