Cart (Loading....) | Create Account
Close category search window
 

Application of the RBF neural networks for tire-road friction force estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Matusko, J. ; Dept. of Control & Comput. Eng. in Autom., Zagreb Univ., Croatia ; Petrovic, Ivan. ; Peric, N.

This paper deals with the problem of the robust tire-road friction force estimation. Good information about friction force generated in contact between wheel and road has significant importance in many active safety systems in modern vehicles (anti-lock brake systems, traction control, vehicle dynamic systems, etc). Since state estimators are usually based on exact model of process, they are therefore limited by the model accuracy. A new estimation scheme based on RBF neural networks is proposed in this paper. The neural network is added to the estimator to compensate the effects of the friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire-road friction force when fiction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel friction model. Simulation results suggest very fast compensation of the changes of the model parameters (< 150 ms) even when they vary in a wide range (changes of 100% and more). Possible drawback of proposed estimation scheme is the fact that neural network does not give the information what particular parameter has changed.

Published in:

Industrial Electronics, 2003. ISIE '03. 2003 IEEE International Symposium on  (Volume:2 )

Date of Conference:

9-11 June 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.