摘要
针对车辆行驶过程中的状态估计问题,提出了基于强跟踪容积卡尔曼滤波的车辆行驶状态估计算法。建立了采用Dugoff轮胎模型非线性3自由度车辆估算模型,通过对纵向加速度、侧向加速度、横摆角速度、转向盘转角和车轮轮速低成本传感器信号的信息融合以实现对车辆行驶状态的准确估计。应用驾驶员模拟器进行在环试验结果表明,基于强跟踪容积卡尔曼滤波的估计算法能够较准确地对车辆行驶状态进行估计。
In order to estimate vehicle state in driving, a vehicle driving state estimation algorithm is proposed based on Strong Tracking Cubature Kalman Filter. The nonlinear 3-DOF model and Dugoff tire model are established, which can accurately estimate vehicle driving state through information integration of the longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle and wheel speed sensor signals. The results of driver simulator in-loop test show that this estimation algorithm based on Strong Tracking Cubature Kalman Filter can accurately estimate the vehicle driving state.
出处
《汽车技术》
北大核心
2015年第9期53-58,共6页
Automobile Technology
基金
国家自然科学基金青年基金资助项目(51305190)
辽宁省教育厅项目(L2013253)
吉林大学汽车仿真与控制国家重点实验室开放基金项目(20111104)
