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

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.