摘要
为更好地表现车辆不同工况下行驶状态,实现相关稳定性控制,在数理分析及模型统一的前提下设计卡尔曼及其衍生算法,对车辆行驶稳定性状态参数质心侧偏角、横摆角速度进行估计对比研究。利用Matlab/Simulink分别建立相关参数估计的算法模型、车辆动力学模型与CarSim进行联合仿真。从仿真时长及误差结果等对算法本身特性、优劣进行验证与分析,传统卡尔曼全工况实时性优势突出,估计精度仅在系统线性状态下有保障;扩展卡尔曼仿真耗时较长,线性及部分非线性状态下估计可靠,非线性强烈下数据表现偏离标准;无迹卡尔曼全工况下估算精度较高,但算法仿真实时性差;容积卡尔曼多变工况下估计精度好、误差分布稳定,且算法仿真实时性次优。
To better represent the driving state of the vehicle under different working conditions and realize the relevant stability control,Kalman and its derivative algorithm are designed on the premise of mathematical analysis and model unification,and the lateral deflection angle of the center of mass and yaw angle velocity of the vehicle are estimated and compared.Matlab/Simulink is employed to build the algorithm model of parameter estimation,vehicle dynamics model and CarSim for co-simulation.The characteristics,advantages and disadvantages of the algorithm are verified and analyzed from the simulation duration and error results.The traditional Kalman has obvious real-time advantage in all working conditions,and the estimation accuracy is only guaranteed in the linear state of the system.Extended Kalman simulation takes a long time,the estimation is reliable under linear and partial nonlinear states,and the data performance deviates from the standard under strong nonlinear states.The estimation accuracy is higher in all working conditions without trace Kalman,but the real-time simulation is poor.Under volumetric Kalman variable condition,the estimation accuracy is high,the error distribution stable,and the real-time simulation of the algorithm sub-optimal.
作者
屈翔
周卓
李亚娟
张君
王伟
QU Xiang;ZHOU Zhuo;LI Yajuan;ZHANG Jun;WANG Wei(Key Laboratory of Advanced Manufacturing Technology for Automobile Parts,Ministry of Education,Chongqing University of Technology,Chongqing 400054,China;Chongqing Tsingshan Industrial Co.,Ltd.,Chongqing 400015,China;School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《重庆理工大学学报(自然科学)》
北大核心
2025年第8期60-68,共9页
Journal of Chongqing University of Technology:Natural Science
基金
重庆市科委应用开发计划项目(cstc2014yykfB70008)。
关键词
车辆动力学
参数估计
卡尔曼滤波
卡尔曼衍生算法
vehicle dynamics
parameter estimation
Kalman filtering
Kalman derived algorithm
