车辆行驶过程中的状态估计问题综述  

Review of Vehicle State Estimation Problem under Driving Situation

作　　者：余卓平[1] 高晓杰[1,2] 

机构地区：[1]同济大学汽车学院,上海201804 [2]上海汽车股份有限公司技术中心,上海201804

出　　处：《机械工程学报》2009年第5期20-33,共14页Journal of Mechanical Engineering

摘　　要：从传感器配置、估计用物理模型、状态估计算法和估计过程中的模型参数自适应4个方面回顾车辆行驶过程中的状态估计问题。对比分析以纵向车速、横摆角速度、质心侧偏角为估计目标时传感器的常见配置,给出合理的传感器配置方案;比较在不同估计目标下运动学模型和动力学模型的优缺点,提出纵向车速和横摆角速度适合采用运动学模型、质心侧偏角估计适合采用动力学模型的观点;列举并比较车辆状态估计中常用的估计算法,给出各算法在实际应用中所需注意的因素。指出实现估计过程中的参数自适应是提高不同行驶工况下观测器估计精度的有效手段,并介绍递推最小二乘、联合卡尔曼滤波等实现参数自适应的典型方法。Vehicle state estimation problem is divided into four aspects： sensor configuration, physical model, estimation algorithm and parameter adaption. The key technologies are later in details discussed. Through comparison of typical sensor configuration for vehicle longitudinal speed, yaw velocity and side-slip angle estimation, an ideal sensor configuration is given. Furthermore, the advantage and disadvantage of kinematic model and dynamic model are analyzed, then the viewpoint is put forward that kinematic model is more suitable for longitudinal speed and yaw velocity estimation, in contrast dynamic model is better for vehicle side-slip angle estimation .The typical estimation algorithms are listed and important issues to be regarded in practical utilization are discussed It is pointed out that the realization of parameter adaption is an effective mothod to enhance the estimation precision under different driving situation. Recursive least square, combined kalman filter and other parameter observation methods are given.

关 键 词：车辆状态估计 观测器 车速估计 横摆角速度估计 质心侧偏角估计 参数自适应 

分 类 号：U270.1[机械工程—车辆工程]