Document Type
Original Study
Abstract
In this paper, the aim of control problem is to achieve required yaw rate and reduce lateral velocity in a short period of time to prevent vehicle from sliding out the curvature. The structure of the controller used consists of modified Elman recurrent neural networks that learned on-line by using genetic algorithm teachings. Using of both front and rear wheels steering simultancously has automatically controlled the vehicle lateral motion when the vehicle rotates the curvature. Therefore, it is used a feedback neural controller that is learned on-line in order to control the transient state output of the system by minimizing the error between the actual output of the system and the model reference output. The eVolutionary techniques based on this algorithm are employed for the model-reference adaptive control scheme for this system.
How to Cite This Article
Sabah, Ahmed
(2006)
"Design of Neuro-Controller for Vehicle Lateral Velocity and Yaw Rate Based Genetic Algorithm With Model Reference Guided.,"
Iraqi Journal of Computers, Communications, Control and Systems Engineering: Vol. 6:
Iss.
1, Article 4.
Available at:
https://ijccce.researchcommons.org/journal/vol6/iss1/4