基于模糊自适应卡尔曼滤波的汽车状态软测量  

Soft Measurement for Vehicle State Based on Fuzzy Adaptive Kalman Filter

作　　者：张袅娜[1] 杜俊杰[1] 

机构地区：[1]长春工业大学电气与电子工程学院,吉林长春130012

出　　处：《自动化技术与应用》2016年第12期95-99,共5页Techniques of Automation and Applications

基　　金：国家基础研究发展计划(973计划)资助项目(编号2011CB711205);吉林省发改委产业技术研究与开发资助项目(编号20 14Y126)

摘　　要：汽车行驶中某些状态信息很难直接测量或测量成本较高,而正确获取这些状态对汽车底盘控制有着重要的意义。为以较低成本获取关键的汽车运动状态,利用参数软测量技术,针对汽车3自由度(3-DOF)动力学模型,采用了基于模糊自适应卡尔曼滤波算法的汽车状态软测量方法 ,利用模糊控制策略实时监测残差的实际方差和理论方差的比值对时变观测噪声的协方差矩阵进行在线修正,通过该方法来抑制噪声对精度的影响,进而提高汽车状态的估计精度。仿真结果表明,本文所采用的模糊自适应卡尔曼滤波算法在时变噪声的情况下能够有效地提高状态估计的精度,能够满足工程实际需求。Some state variables of a vehicle in running are not easy to measure directly or cheaply, however the acquisition of these states has important significance for the chassis control of the vehicle. With soft measurement technique, for three-degree- of （3-DOF） freedom vehicle dynamic model, the soft measurement method of vehicle state is adopted based on fuzzy adaptive Kalman filter （FAKF） algorithm. Fuzzy control strategy is used to by-time monitor the ratio of actual variance and theoretical variance of residuals, and the covariance matrix of time-varying observation noise is modified online. According to the Kalman Filter performs optimally and the accuracy of the state estimation is improved. The simulation results show that fuzzy adaptive Kalman filter can effectively improve the accuracy of state estimation in the case of time-varying noise and meets the needs of engineering.

关 键 词：汽车状态 软测量 自适应卡尔曼滤波 模糊控制策略 

分 类 号：U461.6[机械工程—车辆工程]