Vehicle Stability Improvement Using Fuzzy Controller and Neural-Network Slip Angle Observer 2003-01-2883
This article describes the design and implementation of a fuzzy controller developed for improving car stability by controlling car side-slip angle. The strategy has been to estimate the slip angle by a trained neural network and to determine an appropriate force arrangement on the wheels to produce the necessary yaw moment to limit car side slip control. A seven degrees of freedom car model including nonlinear tire behavior is used in design stage. The results were then validated on a full car model in ADAMS having 156 DOF and including elements nonlinearities and flexibilities. The simulations show the capability of the designed controller in improving stability of the car in sever maneuvers.
Citation: Durali, M. and Bahramzadeh, Y., "Vehicle Stability Improvement Using Fuzzy Controller and Neural-Network Slip Angle Observer," SAE Technical Paper 2003-01-2883, 2003, https://doi.org/10.4271/2003-01-2883. Download Citation
Author(s):
Mohammad Durali, Yusef Bahramzadeh
Affiliated:
Center of Excellence for Design, Robotics and Automation Mechanical Engineering Department Sharif University of Technology P.O. Box 11365-9567, Tehran, Iran
Pages: 6
Event:
International Body Engineering Conference & Exposition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Electronic Braking, Traction, and Stability Controls, Volume 2-PT-129
Related Topics:
Neural networks
Slip
Yaw
Wheels
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