Design of an intelligent feed forward controller system for vehicle obstacle avoidance using neural networks Online publication date: Sat, 19-Jul-2014
by Stratis Kanarachos
International Journal of Vehicle Systems Modelling and Testing (IJVSMT), Vol. 8, No. 1, 2013
Abstract: The design of a novel feed forward controller system for vehicle obstacle avoidance using the neural network methodology is proposed. Currently, most obstacle avoidance systems are designed based on a segmented procedure: a) parametric path planning; b) desired yaw moment computation based on a simplified model; c) yaw moment tracking; d) stable controller design. In this paper, a different strategy is followed. An intelligent 'autopilot', that has been trained using a set of optimised obstacle avoidance manoeuvres, decides how to avoid the obstacle. The obstacle avoidance manoeuvres have been optimised using a reformulation of the Pontryagin's Maximum Principle and global numerical optimisation techniques. The proposed controller has the advantage that it respects 'by design' the internal dynamics of the system and can be adjusted in order to account any model uncertainties. Furthermore, it is computationally very efficient. The performance of the intelligent system is evaluated by means of simulations in MATLAB for a number of test cases.
Online publication date: Sat, 19-Jul-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Systems Modelling and Testing (IJVSMT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com