Title :
A fine control of the air-to-fuel ratio with recurrent neural networks
Author :
Alippi, Cesare ; De Russis, Cosimo ; Piuri, Vincenzo
Author_Institution :
CNR CESTIA, Politecnico di Milano, Italy
Abstract :
A fine control of the air-to-fuel ratio is a fundamental issue to minimise exhaust emissions in automotive fuel injection systems. Traditional approaches have limited effectiveness since the air-to-fuel ratio is sensitive to small engine perturbations, some parts of the combustion process are unknown and some others are nonlinear. In this paper we introduce a direct neural-based control scheme which results in a performance obtainable with more classic approaches based on transient fuel film compensation
Keywords :
air pollution control; internal combustion engines; neurocontrollers; recurrent neural nets; road vehicles; air-to-fuel ratio; automotive fuel injection systems; direct neural-based control scheme; engine perturbations; exhaust emissions minimisation; fine control; recurrent neural networks; Automotive engineering; Combustion; Engines; Exhaust systems; Fuels; Government; Manifolds; Pollution; Recurrent neural networks; Vehicles;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1998. IMTC/98. Conference Proceedings. IEEE
Conference_Location :
St. Paul, MN
Print_ISBN :
0-7803-4797-8
DOI :
10.1109/IMTC.1998.676859
