基于粒子群算法的被动分数阶汽车悬架参数优化设计

Parameters design for passive fractional-order vehicle suspension based on particle swarm optimization

利用粒子群算法研究了被动分数阶汽车悬架参数的优化设计。分数阶汽车悬架系统是指运动微分方程中含有分数阶微分项的汽车悬架系统。建立了被动分数阶悬架系统的仿真模型,利用Oustaloup滤波器算法实现了该模型中分数阶微积分的近似计算。利用粒子群算法寻找一组最优的悬架参数来协调汽车操纵稳定性和乘坐舒适性的关系以到达最优的悬架性能。对比原悬架系统和优化后悬架系统在A、B、C、D共四级路面输入下的响应及其频率特性。研究结果表明,利用该方法对被动分数阶悬架参数进行优化设计,在保证汽车操纵稳定性的前提下乘坐舒适性得到明显改善。

; In this paper, the optimal design of passive fractional-order vehicle suspension parameters was studied based on the particle swarm optimization. A vehicle suspension system whose motion differential equation contains fractional-order derivatives was called as a fractional-order vehicle suspension system. The simulation model of passive fractional-order vehicle suspension system was built, and the approximate solution of fractional-order derivatives in this system was obtained by using the Oustaloup filter algorithm. Then, in order to achieve optimal performance of the passive fractional-order vehicle suspension, the relationship between the riding comfort and driving stability was coordinated by using particle swarm optimization to find a set of optimal suspension parameters. Finally, the responses of original and optimized passive fractional-order suspension systems were compared when the vehicle was running on different road levels from A to D respectively, and their frequency characteristics were also compared. The study results indicate that the ride comfort is improved greatly on the premise of guaranteeing driving stability, when the passive fractional-order vehicle suspension parameters are optimized by using this method.