自适应双层无迹卡尔曼滤波的车辆状态估计  

Vehicle state estimation based on adaptive double-layer untracked Kalman filter

作　　者：徐劲力[1] 张光俊 XU Jingli;ZHANG Guangjun(School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学机电工程学院,武汉430070

出　　处：《重庆理工大学学报（自然科学）》2024年第7期29-36,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：广西科技重大专项项目(桂科AA23062066)。

摘　　要：针对在车辆行驶状态估计中存在估计不准确、鲁棒性较差以及系统噪声不确定等问题,提出一种将双层无迹卡尔曼滤波(DLUKF)与改进的Sage-Husa算法相结合的自适应双层无迹卡尔曼滤波算法(ADLUKF)作为车辆行驶状态的估计器,再结合三自由度汽车模型对车辆行驶的横摆角速度和质心侧偏角进行估计。通过改进的Sage-Husa滤波器对系统过程噪声和测量噪声进行动态调整,进而减少车辆行驶状态估计的误差。应用Carsim与Matlab/Simulink进行联合仿真以及实车试验数据来验证该估计器的有效性,并与无迹卡尔曼滤波(UKF)算法进行对比。结果表明:与UKF算法相比,该算法有效提高了车辆行驶的横摆角速度和质心侧偏角的估计精度和稳定性。Aiming at the issues such as estimation inaccuracy,poor robustness and system noise uncertainty of the unscented Kalman filter(UKF)algorithm in vehicle state estimation,an enhanced Sage-Husa adaptive double-layer unscented Kalman filter(ADLUKF)algorithm is proposed to estimate the yaw velocity and centroid side deflection angle of the vehicle.Through the enhanced Sage-Husa filter,the process noise and measurement noise of the system are dynamically adjusted to achieve the adaptive adjustment of the filter.Meanwhile,a double-layer unscented Kalman filter algorithm is employed to update the initial value of the outer UKF algorithm through the inner UKF algorithm,thereby enhancing the accuracy of the estimation system.To verify the effectiveness of the algorithm,a three-degree-of-freedom vehicle dynamics model is built.Based on this model,a vehicle state estimation algorithm based on ADLUKF and UKF is developed.The effectiveness of the algorithm is verified by using Carsim and Matlab/Simulink co-simulation and real vehicle test data.The results indicate that the ADLUKF algorithm has higher estimation accuracy and better stability compared with UKF.

关 键 词：自适应双层无迹卡尔曼滤波 Sage-Husa 参数估计 横摆角速度 质心侧偏角 

分 类 号：U461.1[机械工程—车辆工程]