中国机械工程2017，Vol.28Issue(6)：750-755,6.DOI:10.3969/j.issn.1004-132X.2017.06.019
基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计
Vehicle State Estimation Based on Combined RLS and FAEKF
摘要
Abstract
For the problems of observation noise time-varying characteristics and model parameter variations in vehicle state estimation,a new algorithm which consisted of RLS method and FAEKF was proposed.The new algorithm was proposed based on 3-DOF nonlinear vehicle dynamics model in order to realize real time update of model parameters and observation noises.Firstly,the total mass of the vehicle was estimated by RLS.Then,a fuzzy controller was established to track the observation noises of extended Kalman filters.Finally,the algorithm was verified using CarSim and MATLAB/Simulink.Results show that the estimation accuracy of the new algorithm is higher than that of the traditional extended Kalman filter.It may provide theoretical support for the development of automo-bile active control systems.关键词
汽车总质量估计/状态估计/递推最小二乘法/模糊自适应扩展卡尔曼滤波Key words
automobile total quality estimation/state estimation/recursive least squares(RLS)/fuzzy adaptive extended Kalman filter(FAEKF)分类
交通工程引用本文复制引用
汪,魏民祥,赵万忠,张凤娇,严明月..基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计[J].中国机械工程,2017,28(6):750-755,6.基金项目
国家自然科学基金资助项目(51375007) （51375007）
江苏省自然科学基金资助项目(SBK2015022352) （SBK2015022352）
常州市科技计划应用基础研究项目(CJ20159011) （CJ20159011）
