|本期目录/Table of Contents|

[1]王少帅,李登峰.基于无迹卡尔曼滤波的路面附着系数估计[J].电子设计工程,2020,28(01):27-31.[doi:10.14022/j.issn1674-6236.2020.01.007]
 WANG Shaoshuai,LI Dengfeng.Road adhesion coefficient estimation based on unscented Kalman filter[J].SAMSON,2020,28(01):27-31.[doi:10.14022/j.issn1674-6236.2020.01.007]
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基于无迹卡尔曼滤波的路面附着系数估计(PDF)
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《电子设计工程》[ISSN:1674-6236/CN:61-1477/TN]

卷:
28
期数:
2020年01期
页码:
27-31
栏目:
计算机技术应用
出版日期:
2020-01-05

文章信息/Info

Title:
Road adhesion coefficient estimation based on unscented Kalman filter
文章编号:
1674-6236(2020)01-0027-05
作者:
王少帅李登峰
(长安大学 电控学院,陕西 西安 710064)
Author(s):
WANG Shao?shuaiLI Deng?feng
(School of Electronics and Control, Chang’an University, Xi’an 710064, China)
关键词:
UKF算法 参数估计 路面附着系数 Dugoff轮胎模型
Keywords:
UKF algorithm parameter estimation road adhesion coefficient Dugoff tire model
分类号:
TP3
DOI:
10.14022/j.issn1674-6236.2020.01.007
文献标志码:
A
摘要:
稳定性和安全性作为汽车设计和规划的两大要素,与车辆的状态参数估算密切相关,其中最直接的就是关于如何精确估算行驶过程中的路面附着系数。在Kalman滤波算法基础之上,本文针对汽车的强非线性特性运用无迹卡尔曼(UKF)滤波算法在Matlab中搭建状态观测器、三自由度车辆模型、Duglff轮胎归一化模型对路面附着系数进行估计,将整体模型与Carsim进行整体测试和对比。结果证明,通过运用UKF算法建立的状态观测器能满足对附着系数估计值的准确性要求。
Abstract:
Stability and safety, as two important elements of vehicle design and planning, are closely related to the estimation of vehicle state parameters, among which the most direct one is how to accurately estimate the adhesion coefficient of road surface during driving. Based on the Kalman filtering algorithm, the Unscented Kalman Filter (UKF) algorithm is used to build the state observer and three-degree-of-freedom vehicle model in Matlab for the strong nonlinear characteristics of the vehicle. The Duglff tire normalization model is used to estimate the adhesion coefficient of the pavement. The whole model is tested and compared with the Carsim model as a whole. The results show that the state observer established by using the UKF algorithm can meet the accuracy requirements for the estimation of the attachment coefficient.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-05-10 稿件编号:201905040基金项目:陕西省重点研发计划项目(2018ZDCXL-GY-05-03-02)作者简介:王少帅(1994—),男,河南三门峡人,硕士。研究方向:分布式驱动、嵌入式系统及应用。
更新日期/Last Update: 2019-12-27