基于改进粒子群算法的刚柔耦合模型车辆平顺性研究
A study on vehicle ride comfort of rigid-flexible coupling model based on advanced particle swarm optimization algorithm
李长艺 1刘壮壮 1李亚洁 1潘成龙 1荣吉利2
作者信息
- 1. 齐鲁工业大学(山东省科学院) 数学与统计学院,山东 济南 250353
- 2. 北京理工大学 宇航学院,北京 100081
- 折叠
摘要
针对刚柔耦合车辆的振动控制问题,建立了半车 7 自由度刚弹耦合动力学模型,用欧拉伯努利梁模型对其进行简化.针对传统粒子群算法容易陷入局部最优解等缺点,引入混沌初始化、动态惯性权重和自适应学习因子对算法进行优化,提出一种基于改进自适应惯性权重的粒子群算法.最后,通过MATLAB仿真建模分析,从车架位移等4 个性能方面将无优化悬架、传统粒子群算法优化和改进粒子群算法优化3 种情况下的结果进行对比.结果表明,在所有指标中改进粒子群算法的结果均显著优于其他二者,证明了该算法的有效性,可以显著改善车辆的行驶平顺性和驾驶稳定性.
Abstract
This paper establishes a half-vehicle 7 degree-of-freedom rigid-flexible coupling dynamic model and simplifies using the Euler-Bernoulli beam model.Given the limitations associated with the traditional PSO algorithm,which include susceptibility to being stuck in local optima and so on.Three methods,namely chaotic initialization,dynamic inertia weight,and adaptive learning factor,are introduced to optimize the algorithm.Accordingly,a refined variant of the PSO algorithm,namely the Adaptive Inertia Weight PSO,has been introduced.Ultimately,by using MATLAB simulation modeling,the outcomes of three scenarios are meticulously compared:the absence of optimization,optimization via the conventional PSO algorithm,and optimization through the enhanced PSO algorithm.These scenarios are evaluated based on four distinct performance aspects.The findings clearly indicate that the refined PSO algorithm surpasses the other two approaches in all metrics,thereby confirming the effectiveness of the proposed algorithm in greatly enhancing the fluidity of vehicle movement and driving stability.
关键词
刚柔耦合/被动悬架/改进的粒子群算法/平顺性Key words
rigid-flexible coupled/passive suspension/improved particle swarm optimization algorithm/ride comfort引用本文复制引用
出版年
2024
NETL
NSTL
万方数据