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李海龙,吕中荣,刘济科.基于改进萤火虫算法的移动车辆参数识
Identification of moving vehicular parameters based on Improved Glowworm Swarm Optimization algorithm[J].计算力学学报,2017,(1):106~110
基于改进萤火虫算法的移动车辆参数识
Identification of moving vehicular parameters based on Improved Glowworm Swarm Optimization algorithm
投稿时间:2015-09-06  最后修改时间:2016-03-25
DOI:10.7511/jslx201701015
中文关键词:  萤火虫优化算法  车桥耦合系统  移动车辆  加速度响应  参数识别
英文关键词:Glowworm Swarm Optimization algorithm  bridge-vehicle system  moving vehicle  acceleration response  parameter identification
基金项目:国家自然科学基金(11172333,11272361)资助项目
作者单位
李海龙 中山大学 力学系, 广州 510275 
吕中荣 中山大学 力学系, 广州 510275 
刘济科 中山大学 力学系, 广州 510275 
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中文摘要:
      提出了一种基于萤火虫优化算法的移动车辆参数直接识别方法。采用四自由度双轴十二参数车辆模型和欧拉梁有限元模型建立了车桥耦合系统的动力方程,并利用Newmark直接积分法求解了系统的动力响应。通过引入一种局部搜索策略和末位淘汰机制改进了萤火虫优化算法的收敛速率,并提高了识别结果的精度。本文方法仅利用车辆振动的竖向加速度响应测量就能进行移动车辆参数的识别。数值算例表明,改进的萤火虫优化算法可以准确地识别出车辆的质量、悬挂刚度和阻尼等参数,并且对测量噪声不敏感。
英文摘要:
      This paper presents an indirect method for the identification of parameters of moving vehicles based on Glowworm Swarm Optimization (GSO) algorithm.Each moving vehicle is modelled as a four-degree-of-freedom system with twelve parameters.The equation of the bridge-vehicle system is established by finite element method.And Newmark direct integration method is used to calculate the dynamic response of the system.A local search method and eliminated system at last one are brought in the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm.Acceleration measurements at selected stations on the vehicle are used to identify the parameters of the moving vehicle with the IGSO algorithm.Several test cases are studied to verify the efficiency of the method and the results show that the proposed method is not sensitive to measurement noise.
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