ITEM METADATA RECORD
Title: Adaptive Neuro-Fuzzy Control with Sliding Mode Learning Algorithm: Application to Antilock Braking System
Authors: Topalov, Andon V
Kayacan, Erdal
Oniz, Yesim
Kaynak, Okyay #
Issue Date: 2009
Publisher: Ieee
Host Document: Ascc: 2009 7th asian control conference, vols 1-3 pages:784-789
Conference: 7th Asian Control Conference (ASCC 2009) Hong Kong, PEOPLES R CHINA, AUG 27-29, 2009
Abstract: A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, which are coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.
ISBN: 978-1-4244-5440-2
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Non-KU Leuven Association publications
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy

 




All items in Lirias are protected by copyright, with all rights reserved.

© Web of science