基于改进萤火虫算法的移动车辆参数识别
发布时间：2018-07-20 21:49
【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。
[Abstract]:A direct identification method for moving vehicle parameters based on firefly optimization algorithm is proposed. The dynamic equations of the vehicle-bridge coupling system are established by using the two-axis 12-parameter vehicle model with four degrees of freedom and the finite element model of the Euler beam. The dynamic response of the system is solved by Newmark direct integration method. The convergence rate of the firefly optimization algorithm is improved by introducing a local search strategy and the final elimination mechanism, and the accuracy of the recognition results is improved. In this paper, the parameters of moving vehicle can be identified only by measuring the vertical acceleration response of vehicle vibration. Numerical examples show that the improved firefly optimization algorithm can accurately identify the vehicle mass, suspension stiffness and damping parameters, and is not sensitive to measurement noise.
【作者单位】： 中山大学力学系;
【基金】：国家自然科学基金(11172333,11272361)资助项目
【分类号】：U446;TP18
，
本文编号：2134899
[Abstract]:A direct identification method for moving vehicle parameters based on firefly optimization algorithm is proposed. The dynamic equations of the vehicle-bridge coupling system are established by using the two-axis 12-parameter vehicle model with four degrees of freedom and the finite element model of the Euler beam. The dynamic response of the system is solved by Newmark direct integration method. The convergence rate of the firefly optimization algorithm is improved by introducing a local search strategy and the final elimination mechanism, and the accuracy of the recognition results is improved. In this paper, the parameters of moving vehicle can be identified only by measuring the vertical acceleration response of vehicle vibration. Numerical examples show that the improved firefly optimization algorithm can accurately identify the vehicle mass, suspension stiffness and damping parameters, and is not sensitive to measurement noise.
【作者单位】： 中山大学力学系;
【基金】：国家自然科学基金(11172333,11272361)资助项目
【分类号】：U446;TP18
，
本文编号：2134899
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