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Traction control using artificial neural networks

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Butler, DA (2002) Traction control using artificial neural networks. Research Master thesis, University of Tasmania.

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Abstract

Modern traction control techniques manage driven wheel speed from data obtained
through a small number of sensors from around an automobile. This data is then fed
into an onboard computer that has been specially programmed to attempt to maximise
driven wheel traction from its information base. Problems can arise, however, when
abnormal conditions are encountered. The small number of sensors used as controller
inputs and the various assumptions made during the conventional controller
programming can lead to erroneous correction commands — with an increased risk of
spinout and other undesirable situations.
The incorporation of artificial neural networks into the traction controller command
logic has the scope to bring a two-fold advantage. The first stems from a neural
network's ability to learn. It can be programmed through experimental data, and thus
removes the need for tedious mathematics programming of the vehicle dynamics, which
can be used to reduce controller cost and also brings about the possibility of continual
reprogramming as the vehicle's condition alters due to wear, etc. The second is its
ability to cope with data from a large number of sensors. With more sensors in place,
the NN has a large base of information on which to make decisions on how to optimise
traction. The result, hopefully, will be a traction control response that will provide
superior traction solutions and be readily programmed to suit different applications.
This study aims at conducting preliminary research into the accuracy and application
limitations of artificial neural networks in the interests of determining their feasibility
for use in closed loop traction control applications. This includes selecting parameters
to be predicted, as well as model input variables, network architectures and the
collection of network training data for off-line control. To this end the accuracy of
backpropagation and general regression neural networks will be compared using 14
input, single output architectures in the interests of predicting longitudinal acceleration,
lateral acceleration and yaw angle, which are identified as crucial factors in vehicle
dynamics control. The study also includes the complete construction of a test vehicle,
in which the sensors are mounted and data acquisition systems used to gather the
network training and testing data, from which this investigation is based. This work is a
step towards establishing the applications of artificial neural networks to automobile
applications on a sound mathematical and quantitative basis.

Item Type: Thesis (Research Master)
Keywords: Automobiles, Neural networks (Computer science), Traction drives, Artificial intelligence
Copyright Holders: The Author
Copyright Information:

Copyright 2002 the Author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s).

Additional Information:

No access until 13 September 2007. Thesis (M.Eng.Sc.)--University of Tasmania, 2002. Includes bibliographical references

Date Deposited: 25 Nov 2014 00:51
Last Modified: 30 Aug 2016 06:08
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