基于车辆动力学和Kalman滤波的汽车状态软测量

Soft Sensor of Vehicle State Based on Vehicle Dynamics and Kalman Filter

针对汽车动力学控制过程中难以在线测得的横摆角速度等状态参数,根据参数软测量理论,采用Kalman滤波并结合汽车两自由度动力学模型,建立了汽车横摆角速度的线性最小均方误差估计算法。仿真计算与场地实验的结果验证了该算法的有效性,同时软测量技术的采用也为汽车控制系统的状态参数测量提供了一条可行、准确且低成本的研究思路。

To solve the problem that some state parameters in vehicle dynamic control process are too difficult to measure on-line, yaw rate estimation algorithm of linear minimize mean square error is established with Kalman filter estimation and two degree-of-freedom vehicle dynamic model, based on parameter soft sensor theory. The results of simulation and field experiment have verified the effectivity of this algorithm and the application of soft sensor technology has provided a feasible, accuracy and low-cost way for the measurement of vehicle state parameter.