基于扩展Kalman粒子滤波的汽车行驶状态和参数估计  

Vehicle State and Parameter Estimation under Driving Situation Based on Extended Kalman Particle Filter Method

作　　者：包瑞新[1,2] 贾敏[1] Edoardo Sabbioni 于会龙[2] 

机构地区：[1]辽宁石油化工大学机械工程学院,抚顺113001 [2]米兰理工大学机械工程学院,米兰20156

出　　处：《农业机械学报》2015年第2期301-306,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家留学基金资助项目(留金发[2013]3018号)

摘　　要：汽车行驶过程中的某些参数通常需要通过实验室内较为昂贵的试验设备获得,测量成本较高,而获取车辆的行驶状态和参数对于车辆行驶过程中的控制有着重要的意义。通常情况下,需要将车辆行驶状态变量和侧偏刚度等参数进行联合估计。这些参数将会被用于车辆动力学模型来分析汽车的操纵状态。本文建立了包含定常统计特性噪声的汽车动力学模型,利用龙格-库塔方法模拟模型,引入扩展Kalman滤波技术,生成粒子滤波重要性概率密度函数,对状态和参数同时进行估计,仿真结果表明,扩展Kalman粒子滤波技术改善了标准粒子滤波算法的精度,验证了算法的有效性。Individual parameters of vehicle dynamic systems were traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure. These parameters were then transferred to vehicle models used at a design stage to simulate the vehicle handling behavior and the cost of measurement was high. At the same time, acquiring the vehicle＇s driving status and parameters had important significance for the process controlling of the vehicle. Normally, the status and parameter of the test vehicle needed to be estimated together, which were then transferred to vehicle models and used at a design stage to simulate the vehicle handling behavior. A vehicle dynamics system containing constant noise and non-linear model was established,Runge - Kutta method was used to simulate the model. The extended Kalman filter algorithm was used as the importance density function to update particles in particle filter, with which the local state estimated values and parameters can be calculated. The simulation results showed that the proposed algorithm improved the accuracy of standard particle filter.

关 键 词：车辆 扩展Kalman滤波 粒子滤波动力学模型 龙格-库塔方法 

分 类 号：U461.6[机械工程—车辆工程]