Design of fuzzy controller for magneto-rheological suspension using a hybrid Taguchi genetic algorithm to improve ride quality

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.