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Estimation of brake force on an open wheel racing car using artificial neural networks

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Heron, GC (2002) Estimation of brake force on an open wheel racing car using artificial neural networks. Research Master thesis, University of Tasmania.

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Abstract

The nature of automobile dynamics is complex. While they might not be aware of it, the
driver of a vehicle is making many complex decisions producing a complex series of
actions that effect the motion of the vehicle. Usually the driver can perform a sequence
of actions which move the vehicle in a way in which the driver intends, however,
occasionally all drivers find themselves having to correct the vehicle in a way that they
did not expect.
The problem here is in the control system and the high performance available in the
vehicles that are driven on the roads. With the brake pedal linked directly to the force
on the brake disks, the driver of the vehicle simply applies pressure that corresponds to
the rate at which they intend to stop. At the limits of tyre adhesion this breaks down as
more brake pressure fails the slow the vehicle quicker, and the vehicle actually takes
longer to stop.
To produce safer vehicles, car developers and manufacturers have developed anti-lock
brakes and stability control systems. These state of the art systems monitor driver
commands that inherently reflect their intention and the behaviour of the vehicle. When
the vehicle behaves in a way that does not follow the driver's intent the system
intervenes and selectively applies braking, limits engine power or changes other
relevant parameters to assist the driver in retaining control of the vehicle. Systems such
as these use mathematical models based on simplified assumptions of vehicle
behaviour. Because of this they are built to be robust, commercially available systems
fail to capitalise on the full performance potential of the vehicle. Since systems such as
these become active in emergency situations, every small gain in performance can make
up the difference between life and death.
Neural networks, as emerging decision making tools offer another approach to the
problem of modelling non-linear dynamic, multi variable vehicle physics. Neural
networks use artificial intelligence to find relationships between inputs and outputs.
These relationships are not assumed or based upon a simplified physical analysis, but
are built based on the past experiences of the network.
For automobile dynamic prediction a special vehicle 'Intelligent Car' was conceived,
constructed and tested in real world driving conditions. Structured driving tests were
carried out gathering sufficient data to train ant test the potential of neural networks for
the application. The results from these tests represent some of the first outcomes from
preliminary research in the 'Intelligent Car' Project. This study outlines the design and development of the University of Tasmania's Intelligent Car together with results from training various neural network models to
predict its brake forces and comparison with measured values.

Item Type: Thesis (Research Master)
Keywords: Intelligent Car Project, Neural networks (Computer science), Automobiles, Automobiles, Driver assistance systems
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 10 April 2007. Thesis (M.Eng.Sc.)--University of Tasmania, 2002. Includes bibliographical references

Date Deposited: 19 Dec 2014 02:44
Last Modified: 02 Dec 2016 01:58
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