四驱车辆交互式多模型自适应无迹卡尔曼滤波路面附着系数估计  

Estimation of Road Adhesion Coefficient Using Interactive Multiple Model Adaptive Unscented Kalman Filter for 4WID Vehicles

作　　者：邓浩楠 赵治国[1] 赵坤 李刚 于勤 Deng Haonan;Zhao Zhiguo;Zhao Kun;Li Gang;Yu Qin(School of Automotive Studies,Tongji University,Shanghai 201804;Lotus Automobile Company limited,Wuhan 430000)

机构地区：[1]同济大学汽车学院,上海201804 [2]武汉路特斯汽车有限公司,武汉430000

出　　处：《汽车工程》2024年第8期1357-1369,共13页Automotive Engineering

基　　金：国家自然科学基金(52172390)资助。

摘　　要：路面附着系数对车辆动力学控制性能有重要影响,为准确实时估计路面附着系数,提高算法在不同路面及工况下的估计精度与收敛速度,本文针对分布式四轮驱动车辆,结合7自由度车辆动力学模型和Dugoff轮胎模型,提出了一种基于交互式多模型的自适应无迹卡尔曼滤波(IMM-AUKF)路面附着系数估计方法,首先将改进的Sage-Husa噪声估计器引入到无迹卡尔曼滤波(UKF)算法中,构建了自适应无迹卡尔曼滤波(AUKF)观测器,以对测量噪声进行实时更新并保证其协方差矩阵的正定性,同时提高新观测数据的权重,并增强算法的实时跟踪精度和稳定性;然后通过选择不同的观测变量,分别构建了车辆纵向行驶工况AUKF观测器和横纵向耦合工况AUKF观测器,并利用交互式多模型(IMM)算法进行观测器模型的切换,进而实现算法在车辆不同行驶工况下路面附着系数的准确估计。高附、低附、对接以及对开等路面仿真试验及实车道路试验结果表明,所提出的IMM-AUKF算法相比于传统的UKF算法,具有更高的估计精度与更快的收敛速度,能够适应不同工况下路面附着系数的实时准确估计。The road adhesion coefficient has an important impact on the vehicle dynamics control performance.In order to accurately obtain the road adhesion coefficient in real time and improve the estimation accuracy and convergence speed of the algorithm under different road surfaces and driving conditions,an interactive multiple model adaptive unscented Kalman filter(IMM-AUKF)based on the seven-degree-of-freedom vehicle dynamics model and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles.The algorithm first introduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer,which updates the measurement noise in real time and ensures the positive characterization of its covariance matrix,improves the weight of the new observation data,and enhances the real-time tracking accuracy and stability of the algorithm.Afterwards,the algorithm selects different observation variables to construct the longitudinal driving condition AUKF observer and the lateral-longitudinal coupling driving condition AUKF observer.And the IMM algorithm is also used to switch the observer model,so as to realize the algorithm's accurate estimation of the road adhesion coefficient under different driving conditions.The results of simulation tests on high/low attachment,joint and u-split roads and real vehicle road tests show that the proposed IMM-AUKF algorithm has higher estimation accuracy and faster convergence speed than the traditional UKF algorithm,and it can adapt to the real-time and accurate estimation of the road adhesion coefficient under different driving conditions.

关 键 词：分布式四轮驱动 路面附着系数 交互式多模型 自适应无迹卡尔曼滤波 

分 类 号：U461.51[机械工程—车辆工程] TN713[交通运输工程—载运工具运用工程]