Research on semi-active suspension variable weight model predictive control under car-following scenario

In view of the automotive comfort under high-speed following conditions, a semi-active suspension control method based on model predictive control is proposed. The performance function of the controller consists of the predicted output of the vehicle, the output force of the actuator, and their respective weights. Usually, the selection of the weights requires sufficient engineering experience and a large number of experiments to determine. There are mainly three working conditions for constant speed, acceleration and braking conditions, and different weight groups are proposed for different driving conditions. Therefore, the Particle swarm algorithm (PSO) is used to optimize the parameters of the MPC controller, and in order to obtain three groups of weights under different driving conditions. The simulation results show that compared with the passive suspension system, the MPC controller based on the particle swarm algorithm reduces the RMS value of the vertical acceleration of the vehicle under the three working conditions, so the suspension control strategy effectively improves the ride comfort of passengers.