Title: Application of neural networks to the automotive engine problem

Authors: M.M. Ramli, A.S. Morris

Addresses: Dept. of Automatic Control and Systems Engineering, University of Sheffield, UK. ' Dept. of Automatic Control and Systems Engineering, University of Sheffield, UK

Abstract: Model uncertainty is a serious problem facing control engineers. Often it is not possible to adequately represent system characteristics such as non-linearity, time delay, saturation, time-varying parameter, and overall complexity. This is particularly true in automotive control and modelling. To tackle this problem, this paper discusses an artificial intelligence method called artificial neural networks. After discussing the basics of artificial neural network, a controller that incorporates a neural network with a self-tuning controller is formulated. Then the neural network is applied to a particular engine problem. The network was used to track the system|s dynamic using only the input and output data.

Keywords: back propagation; artificial neural networks; ANNs; learning methods; model uncertainty; vehicle control; vehicle modelling; self-tuning controllers; engine control; vehicle design.

DOI: 10.1504/IJVD.1993.061833

International Journal of Vehicle Design, 1993 Vol.14 No.2/3, pp.184 - 193

Published online: 28 May 2014 *

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