Active Suspension Control Based on Particle Swarm Optimization

Abstract

Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state to enhance the performance of active suspension that has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research directions for active suspension patents. Objective: The numerical analysis method is proposed to study the performance characteristics of the active suspension controlled by different controllers. Methods: The active suspension control model and control strategy based on particle swarm optimization are established, and two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed. Moreover, two active suspension systems are optimized by particle swarm optimization. Results: The results of the analysis show that the performance of the active suspension is significantly improved compared with the passive suspension when the vehicle runs on the same road. The ride comfort of the active suspension controlled by the fuzzy PID controller has the best adaptive performance when the vehicle runs on different grade roads or white noise roads. The active suspension controlled by the fuzzy PID controller has the best ride comfort. Conclusion: A good control strategy can effectively improve the performance of the active suspension. To improve the performance of the active suspension, it can be controlled by utilizing different control strategies. The results lay a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.