A comparative study on identification of vehicle inertial parameters

Author

Zarringhalam, R. ; Rezaeian, A. ; Melek, William ; Khajepour, Amir ; Shih-Ken Chen ; Moshchuk, N.

Author_Institution

Mech. & Mechatron. Eng. Dept., Univ. of Waterloo, Waterloo, ON, Canada

fYear

2012

fDate

27-29 June 2012

Firstpage

3599

Lastpage

3604

Abstract

This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.

Keywords

Kalman filters; control engineering computing; driver information systems; gradient methods; least squares approximations; mechanical engineering computing; mechanical stability; nonlinear filters; recursive filters; road vehicles; vehicle dynamics; CarSim; RKF; RLS; active vehicle control; center-of-gravity location; driver assistant systems; extended Kalman filter; gradient Kalman filter; mass estimation; moment-of-inertia estimation; recursive Kalman filter; recursive least squares; simulation analysis; vehicle inertial parameter identification; vehicle stability; Equations; Estimation; Kalman filters; Mathematical model; Vehicle dynamics; Vehicles; Wheels;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2012

Conference_Location

Montreal, QC

ISSN

0743-1619

Print_ISBN

978-1-4577-1095-7

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2012.6314832

Filename

6314832