改进强跟踪滤波算法及其在汽车状态估计中的应用

周聪; 肖建

doi:10.3724/SP.J.1004.2012.01520

改进强跟踪滤波算法及其在汽车状态估计中的应用

doi: 10.3724/SP.J.1004.2012.01520

周聪,
肖建

1.
西南交通大学电气工程学院成都 610031

详细信息

通讯作者:
周聪

计量
- 文章访问数: 2190
- HTML全文浏览量: 113
- PDF下载量: 999
- 被引次数: 0
出版历程
- 收稿日期: 2011-08-31
- 修回日期: 2012-04-10
- 刊出日期: 2012-09-20

Improved Strong Track Filter and Its Application to Vehicle State Estimation

1.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031

摘要

摘要: 准确实时地获取汽车行驶过程中的状态变量,对汽车底盘控制有着重要的意义,而这些关键状态往往难以直接测量或者成本较高.结合纵向、侧向和横摆三自由度非线性汽车模型,将改进强跟踪滤波(Improved strong track filter, ISTF)算法应用到汽车的状态估计中,并改进了算法的稳定性.与扩展卡尔曼滤波(Extended Kalman filter, EKF)算法进行比较分析.通过Carsim和Matlab/Simulink联合仿真和实车双移线实验验证算法,结果表明,该算法在估计精度、跟踪速度、抑制噪声等方面均优于扩展卡尔曼滤波算法,满足汽车状态估计器的软件性能要求.
- 汽车动力学 /
- 状态估计 /
- 卡尔曼滤波器 /
- 强跟踪滤波器
Abstract: Accurate and real time acquirement of vehicle state variables in running is of great significance to vehicle chassis control. However, these key state variables are not easy to measure directly or cheaply. An improved strong track filter (ISTF) which is much more stable is introduced in this paper. By using a nonlinear 3 degree-of-freedom vehicle model including longitudinal motion, lateral motion, and yaw motion, a state estimation algorithm was established and applied to vehicle state estimation. Comparison was made between extended Kalman filter (EKF) and ISTF. A double lane change test was carried out on Carsim and Matlab/Simulink co-simulation as well as on a real vehicle. The results showed that ISTF is better than EKF in the estimating accuracy, tracking speed, and restraining noise. It was proved that ISTF can satisfy the requirements of vehicle state estimation.
- Vehicle dynamics /
- state estimation /
- Kalman filter (KF) /
- strong track filter (STF)

HTML全文

参考文献(1)

[1]

Venhovens Pau J T, Naab Karl. Vehicle dynamics estimation using Kalman filters. Vehicle System Dynamics, 1999, 32(2-3): 171-184[2] Gustaffson F, Persson N, Drev M, Lofgren M. Virtual sensors of tire pressure and road friction. In: Proceedings of the 2001 Society of Automotive Engineers World Congress. Detroit, USA: SAE, 2001. 2001-01-0796[3] Wenzel T A, Burnham K J, Blundell M V, Williams R A. Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics, 2006, 44(2): 153-171[4] Wenzel T A, Burnham K J, Blundell M V, Williams R A. Kalman filter as a virtual sensor: applied to automotive stability systems. Transactions of the Institute of Measurement and Control, 2007, 29(2): 95-115[5] Tseng H E. A sliding mode lateral velocity observer. In: Proceedings of the 6th International Symposium on Advanced Vehicle Control. Hiroshima, Japan: AVEC, 2002. 387-392[6] Stéphant J, Charara A, Meizel D. Virtual sensor: application to vehicle sideslip angle and transversal forces. IEEE Transactions on Industrial Electronics, 2004, 51(2): 278-289[7] Zhou Dong-Hua, Xi Yu-Geng, Zhang Zhong-Jun. A suboptimal multiple fading extended Kalman filter. Acta Automatica Sinica, 1991, 17(6): 689-695(周东华, 席裕庚, 张钟俊. 一种带多重次优渐消因子的扩展卡尔曼滤波器. 自动化学报, 1991, 17(6): 689-695)[8] Zhou Dong-Hua. Introduction to Adaptive Control of Non-Linear Systems. Beijing: Tsinghua University Press, 2002. 44-75 (周东华. 非线性系统的自适应控制导论. 北京: 清华大学出版社, 2002. 44-75)[9] Zhou Dong-Hua, Ye Yin-Zhong. Modern Fault Diagnosis and Fault-Tolerant Control. Beijing: Tsinghua University Press, 2000. 60-76 (周东华, 叶银忠. 现代故障诊断与容错控制. 北京: 清华大学出版社, 2000. 60-76)[10] Arbor A. CarSim Reference Manual. USA: Mechanical Simulation Corporation, 2005. 30-53

施引文献

资源附件(0)

访问统计