Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization

This study proposes a novel variable universe fuzzy control design for vehicle semi-active suspension system with magnetorheological (MR) damper through the combination of fuzzy neural network (FNN) and particle swarm optimization (PSO). By constructing a quarter-vehicle test rig equipped with MR damper and then collecting the measured data, a non-parametric model of MR damper based on adaptive neuro-fuzzy inference system is first presented. And then a Takagi-Sugeno (T-S) fuzzy controller is designed to achieve the effective control of the input current in MR damper by using the contraction-expansion factors. Furthermore, an appropriate FNN controller is proposed to obtain the contraction-expansion factors, in which particle swarm optimization and back propagation are introduced as the learning and training algorithm for the FNN controller. Lastly, a simulation investigation is provided to validate the proposed control scheme. The results of this study can provide the technical foundation for the development of vehicle semi-active suspension system.