Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model

Zhao Pan, Changfu Zong, Dan Hu, Hongyu Zheng, Kan Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

It is difficult to obtain state variables accurately or economically while vehicle is moving, however these state variables are significant for chassis control. Although many researches have been done, a complex model always leads to a control system with poor real-time performance, while simple model cannot show the real characteristics. So, in order to estimate the value of yaw rate and side slip angle accurately and sententiously, an Extended Kalman Filter (EKF) observer is proposed, which is based on an ameliorated 2-DOF “bicycle model”. The EKF algorithm is achieved numerically and verified by the results from the real field test.
Original languageEnglish
JournalSAE Technical Papers
DOIs
Publication statusPublished - 23 Jun 2008

Fingerprint

Extended Kalman filters
Bicycles
Chassis
Control systems

Cite this

Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model. / Pan, Zhao; Zong, Changfu; Hu, Dan; Zheng, Hongyu; Wu, Kan .

In: SAE Technical Papers, 23.06.2008.

Research output: Contribution to journalArticle

@article{b9720f6770f0484e8ff0adc4779e8cb9,
title = "Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model",
abstract = "It is difficult to obtain state variables accurately or economically while vehicle is moving, however these state variables are significant for chassis control. Although many researches have been done, a complex model always leads to a control system with poor real-time performance, while simple model cannot show the real characteristics. So, in order to estimate the value of yaw rate and side slip angle accurately and sententiously, an Extended Kalman Filter (EKF) observer is proposed, which is based on an ameliorated 2-DOF “bicycle model”. The EKF algorithm is achieved numerically and verified by the results from the real field test.",
author = "Zhao Pan and Changfu Zong and Dan Hu and Hongyu Zheng and Kan Wu",
year = "2008",
month = "6",
day = "23",
doi = "10.4271/2008-01-1783",
language = "English",
journal = "SAE Technical Papers",
issn = "0148-7191",
publisher = "SAE International",

}

TY - JOUR

T1 - Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model

AU - Pan, Zhao

AU - Zong, Changfu

AU - Hu, Dan

AU - Zheng, Hongyu

AU - Wu, Kan

PY - 2008/6/23

Y1 - 2008/6/23

N2 - It is difficult to obtain state variables accurately or economically while vehicle is moving, however these state variables are significant for chassis control. Although many researches have been done, a complex model always leads to a control system with poor real-time performance, while simple model cannot show the real characteristics. So, in order to estimate the value of yaw rate and side slip angle accurately and sententiously, an Extended Kalman Filter (EKF) observer is proposed, which is based on an ameliorated 2-DOF “bicycle model”. The EKF algorithm is achieved numerically and verified by the results from the real field test.

AB - It is difficult to obtain state variables accurately or economically while vehicle is moving, however these state variables are significant for chassis control. Although many researches have been done, a complex model always leads to a control system with poor real-time performance, while simple model cannot show the real characteristics. So, in order to estimate the value of yaw rate and side slip angle accurately and sententiously, an Extended Kalman Filter (EKF) observer is proposed, which is based on an ameliorated 2-DOF “bicycle model”. The EKF algorithm is achieved numerically and verified by the results from the real field test.

U2 - 10.4271/2008-01-1783

DO - 10.4271/2008-01-1783

M3 - Article

JO - SAE Technical Papers

JF - SAE Technical Papers

SN - 0148-7191

ER -