多轴重型特种车辆具有载重大、 质心高、速度快的特点，在高机动行驶条件下存在发生操纵失稳的可能性。质心侧偏角和横摆角速度是衡量车辆操稳性的关键判据，如果直接通过车载传感器测量存在测量困难、成本昂贵和长时误差等问题，因此开展了对五轴重型车辆的状态估计研究。首先基于Matlab/Simulink搭建了五轴特种车辆的13自由度动力学模型，利用行驶状态监测系统进行实车实验，验证模型的精确性，为估计模型提供理论依据及验证仿真平台。然后建立非线性三自由度车辆模型，基于无迹卡尔曼滤波算法，设计关于横摆角速度、质心侧偏角、纵向速度的状态估计器。最后，以13自由度模型为仿真平台分别进行角阶跃输入、脉冲输入及正弦输入工况的验证。仿真结果证明，基于非线性三自由度车辆模型的状态估计器可实现对纵向速度、横摆角速度和质心侧偏角参数的动态估计。
Abstract
Multi-axle heavy-duty special vehicles have the characteristics of heavy load, high center of mass, and fast speed, and there is the possibility of handling instability under high maneuvering conditions. The center of mass slip angle and the yaw rate are the key criteria for measuring the stability of the vehicle. If directly measured by the on-board sensor, there are problems such as difficulty in measurement, high cost, and long-term error. Therefore, a study on the state estimation of five-axle heavy vehicles has been carried out.. Research. First, a 13-degree-of-freedom dynamic model of a five-axle special vehicle was built based on Matlab/Simulink, and a real-vehicle experiment was carried out using the driving condition monitoring system to verify the accuracy of the 13-degree-of-freedom model, providing a theoretical basis for the estimation model and a verification simulation platform. Then a nonlinear three-degree-of-freedom vehicle model is established. Based on the unscented Kalman filter algorithm, state estimators for yaw rate, center of mass slip angle, and longitudinal velocity are designed. Finally, the 13-degree-of-freedom model is used as the simulation platform to verify the angular step input, pulse input and sinusoidal input conditions. The results show that the state estimator based on the nonlinear three-degree-of-freedom vehicle model can realize the dynamic estimation of the parameters of longitudinal velocity, yaw rate and side slip angle of centroid.
关键词
五轴重型车辆 /
动力学模型 /
实车验证 /
无迹卡尔曼滤波 /
状态估计
{{custom_keyword}} /
Key words
five-axle heavy vehicle /
dynamic model /
real vehicle verification ；Unscented Kalman Filter /
Simulink /
parameter estimation
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] LI L, ZHOU Q. A robust observer designed for vehicle lateral motion estimation[M]. 2005. 2005:417-422.
[2] CHEN L, CHEN T, XU X, et al. Multi-Objective Coordination Control Strategy of Distributed Drive Electric Vehicle by Orientated Tire Force Distribution Method[J]. IEEE Access, 2018, 6:69559-69574.
[3] 张亮修, 周静, 成强, et al. 考虑路面不平输入的车辆侧倾状态估计及主动控制[J]. 振动与冲击, 2018, 37(5):10.
Zhang Liangxiu, Zhou Jing, Cheng Qiang, et al. Estimation and active control of vehicle roll state considering road roughness input [J]. Vibration and Shock, 2018, 37(5):10.
[4] ANTONOV S, FEHN A, KUGI A. Unscented Kalman filter for vehicle state estimation[J]. Vehicle System Dynamics, 2011, 49:1497-1520.
[5] 赵万忠, 张寒, 王春燕. 基于无迹卡尔曼滤波的汽车状态参数估计[J]. 华南理工大学学报（自然科学版）, 2016, 44(3):76-80,88.
Zhao Wanzhong, Zhang Han, Wang Chunyan. Estimation of Vehicle State Parameters Based on Unscented Kalman Filter [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(3):76-80,88.
[6] DOUMIATI M, VICTORINO A, CHARARA A, et al. Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering[M]. 2009:4804-4809.
[7] GU X, CHEN G, ZONG C. Based on the Unscented Kalman Filter to Estimate the State of Four-Wheel-Independent Electric Vehicle with X-by-Wire[J]. SAE International Journal of Commercial Vehicles, 2015, 8:316-322.
[8] 余志生. 汽车理论[M]. 北京：机械工业出版社, 2008.
Yu Zhisheng. Automobile Theory[M]. Beijing: Machinery Industry Press, 2008.
[9] 李佳欣, 杨波, 裴晓飞. 基于Simulink/Trucksim的四轴分布式电驱动车辆的建模与仿真 [J]. 数字制造科学, 2020, 18(01):97-103.
Li Jiaxin, Yang Bo, Pei Xiaofei. Modeling and Simulation of Four-axis Distributed Electric Drive Vehicle Based on Simulink/Trucksim [J]. Digital Manufacturing Science, 2020, 18(01):97-103.
[10] 黄通, 高钦和, 刘志浩, et al. 重载子午轮胎非线性特性研究与模型修正 [J]. 华中科技大学学报(自然科学版), 2021, 49(10):42-46.
Huang Tong, Gao Qinhe, Liu Zhihao, et al. Study on nonlinear characteristics and model correction of heavy-duty radial tires [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(10):42-46.
[11] 张亮修, 吴光强, 王宇. 车辆纵-侧-垂向动力学统一建模及实车验证[J]. 机械工程学报, 2019, (16):9.
Zhang Liangxiu, Wu Guangqiang, Wang Yu. Unified Modeling and Real Vehicle Verification of Vehicle Longitudinal-Side-Vertical Dynamics [J]. Chinese Journal of Mechanical Engineering, 2019, (16):9.
[12] 张一西, 马建, 赵轩, et al. 基于蚁狮算法的UKF车辆状态参数估计器[J]. 中国公路学报, 2020, v.33;No.201(05):169-181.
Zhang Yixi, Ma Jian, Zhao Xuan, et al. UKF vehicle state parameter estimator based on ant lion algorithm [J]. Chinese Journal of Highways, 2020, v.33;No.201(05):169-181.
[13] 王震坡, 薛雪, 王亚超. 基于自适应无迹卡尔曼滤波的分布式驱动电动汽车车辆状态参数估计[J]. 北京理工大学学报, 2018, 38(07):698-702.
Wang Zhenpo, Xue Xue, Wang Yachao. Vehicle State Parameter Estimation for Distributed Drive Electric Vehicles Based on Adaptive Unscented Kalman Filtering [J]. Journal of Beijing Institute of Technology, 2018, 38(07):698-702.
[14] 黄小平, 王岩. 卡尔曼滤波原理及应用:MATLAB仿真[M]. 北京：电子工业出版社, 2015.
Huang Xiaoping, Wang Yan. The principle and application of Kalman filter: MATLAB simulation[M]. Beijing: Electronic Industry Press, 2015.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(3553 KB)
