Active Suspension Control Based on Particle Swarm Optimization

(E-pub Abstract Ahead of Print)

Author(s): Shaobin Lv, Guoqiang Chen*, Jun Dai.

Journal Name: Recent Patents on Mechanical Engineering

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Abstract:

Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state, so that the active suspension has outstanding performance and has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research direction 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 the particle swarm optimization are established, two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed, and two active suspension systems are optimized by the particle swarm optimization.

Results: The analysis results 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, the active suspension can be controlled by utilizing different control strategies. The results lays a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.

Keywords: Active suspension, Fuzzy PID controller, Numerical analysis method, Particle swarm optimization, Performance characteristic, Sliding mode controller.

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/2212797612666191118123838
Price: $95