Nonlinear Damping Curve Control of Semi-Active Suspension Based on Improved Particle Swarm Optimization

Abstract

This paper studies the semi-active suspension damping control algorithm. The shortcomings of traditional damping control algorithms such as the sky-hook algorithm and the acceleration-driven damper algorithms are analyzed in this article. For the shortcomings of the traditional damping control algorithm, a semi-active suspension damping control strategy based on improved particle swarm optimization is proposed. The proposed algorithm uses the dynamic nonlinear inertia coefficient instead of the fixed inertia coefficient of the traditional particle swarm algorithm, which improves solution efficiency and accuracy. Nonlinear damping curves for different forms of expression are optimized using the algorithm, and the optimal nonlinear damping curve for the white noise pavement is obtained. A simulation model is established to verify the effect of the proposed algorithm. The simulation results show that the nonlinear semi-active control strategy can achieve the target of high vibration isolation coefficient at high frequency and strong inhibitory resonance ability at low frequency. Moreover, the jerk of the spring mass does not deteriorate by a large margin. The results show that the nonlinear semi-active control strategy improves the driving comfort of the vehicle.