Title: Neural network models for multi-step ahead prediction of air-fuel ratio in SI engines

Authors: Samir Saraswati, Satish Chand

Addresses: Mechanical Engineering Department, MNNIT, Allahabad, 211004, India. ' Mechanical Engineering Department, MNNIT, Allahabad, 211004, India

Abstract: The non-linear dynamics present in SI engine combined with transport delay, limits the performance of the engine controller. Identifying the air-fuel ratio, few steps in advance can help the engine controller to take care of these. In the present work, various neural network models are evaluated for multi-step ahead prediction of air-fuel ratio. Neural network models are trained and validated using uncorrelated data generated from engine simulations in Matlab/Simulink® environment. It is shown that a neural network autoregressive model with exogenous inputs (NNARX) and a neural network autoregressive moving average model with exogenous input (NNARMAX) are able to predict engine simulations with reasonably good accuracy.

Keywords: neural networks; engine modelling; air-fuel ratio control; air-fuel ratio sensors; SI engines; spark ignition; nonlinear dynamics; simulation.

DOI: 10.1504/IJMIC.2009.027213

International Journal of Modelling, Identification and Control, 2009 Vol.7 No.3, pp.263 - 274

Published online: 17 Jul 2009 *

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