Title :
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
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;
Conference_Titel :
American Control Conference (ACC), 2012
Conference_Location :
Montreal, QC
Print_ISBN :
978-1-4577-1095-7
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2012.6314832
