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Journal Article

Citation

El-Gindy M, Palkovics L. Int. J. Veh. Des. 1993; 14(5-6): 592-614.

Affiliation

Natl Research Council of Canada, Ottawa, Can

Copyright

(Copyright © 1993, Inderscience Publishers)

DOI

unavailable

PMID

unavailable

Abstract

It is a well-known fact that difficulties are generally encountered when mathematical modelling is performed on vehicle systems and sub-systems that are inherently non-linear. Conventional modelling techniques - simulation, where non-linearities are approximated by simple mathematical formulas or look-up tables; finite element approximation, where the system is replaced by a linear model if the range of variables is small; and numerical techniques - have been very successful in modelling complex, non-linear systems. Under certain conditions, however, the approximations and hypotheses developed for each of these models fail to reflect the true behaviour of the system. The non-linearity of vehicle systems and sub-systems is particularly severe when the vehicle is pushed toward its performance limits. The result is that conventional modelling techniques become progressively more inaccurate. Recent developments in the area of artificial neural networks (ANNs) may provide an alternative approach to the modelling of vehicular dynamics, particularly for highly non-linear systems that near their performance limits. The objective of this paper is to direct attention to the possible applications of ANNs to vehicle system dynamics and control. A literature search of work related to the application of ANNs to vehicle systems is presented and an attempt made to propose possible applications of ANNs to vehicle dynamics and control.

Language: en

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