摘要
基于粒子滤波和卡尔曼滤波的轨道车辆二系悬挂系统参数估计方法存在因粒子枯竭而无法对系统参数变化进行监测的缺陷,所以引入再次均匀采样策略对其进行改进。采用轨道车辆系统横向动力学模型,建立轨道车辆二系悬挂系统的横向动态空间模型,并运用改进后的参数估计算法和Matlab软件对二系横向阻尼和抗蛇行阻尼2个系统参数进行仿真计算。结果表明:在无法获得轨道横向不平顺统计特性的情况下,参数估计方法仍具有良好的鲁棒性;在轨道车辆二系悬挂系统发生突发或老化故障的情况下,改进后的参数估计方法可以准确估计系统参数的变化,且误差值始终保持在10%以下,能够实现对系统状态的实时监测。
The estimation method, which is based on particle filter and Kalman filtering, for the secondary suspension system parameters of railway vehicle has the defect that parameter change can＇t be monitored due to particle depletion, so repeated uniform sampling strategy is introduced to improve it. The lateral dynamics model of railway vehicle system is used to establish the lateral dynamic space model for the sec- ondary suspension system of railway vehicle. Simulation is carried out on the system parameters （seconda- ry lateral damping and anti-yaw damping） by the improved parameter estimation algorithm and Matlab software. Results show that parameter estimation method still has good robustness if the statistical prop- erties of track lateral irregularity can＇t be accessed. The improved parameter estimation method can accu- rately estimate the changes of system parameters when sudden and aging failures of secondary suspension system happen. And the error values always remain below 10%, so the real-time monitoring to the system state can be achieved successfully.
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2013年第3期72-78,共7页
China Railway Science
基金
上海市科委部分地方院校能力建设项目(12210501200)
上海市教育委员会科研创新项目(12YZ150)
关键词
轨道车辆
悬挂系统
状态监测
参数估计
粒子滤波
再次均匀采样
Railway vehicle
Suspension system
Condition monitoring
Parameter estimation
Particle filter
Repeated uniform sampling
