摘要
针对汽车状态估计中模型参数的变化和观测噪声的时变特性,提出了递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的汽车状态估计算法。为实现模型参数与观测噪声的实时更新,建立了基于三自由度非线性车辆动力学模型的算法,首先利用递推最小二乘法对汽车的总质量进行估计,其次建立了模糊控制器对扩展卡尔曼滤波的观测噪声进行实时跟踪。在搭建的CarSim与MATLAB/Simulink联合仿真平台中验证了该算法的有效性,结果表明该算法估计精度高于传统扩展卡尔曼滤波算法,研究结果为汽车的主动安全控制提供了理论支持。
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 automobile active control systems.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2017年第6期750-755,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51375007)
江苏省自然科学基金资助项目(SBK2015022352)
常州市科技计划应用基础研究项目(CJ20159011)
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
automobile total quality estimation
state estimation
recursive least squares（RLS）
fuzzy adaptive extended Kalman filter（FAEKF）
