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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003 259 A Neural-Network Based Control Solution
 

Summary: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003 259
A Neural-Network Based Control Solution
to Air-Fuel Ratio Control for Automotive
Fuel-Injection Systems
Cesare Alippi, IEEE Senior Member, Cosimo de Russis, and Vincenzo Piuri, IEEE Fellow
Abstract--Maximization of the catalyst efficiency in automotive
fuel-injection engines requires the design of accurate control sys-
tems to keep the air-to-fuel ratio at the optimal stoichiometric value
AF . Unfortunately, this task is complex since the air-to-fuel ratio
is very sensitive to small perturbations of the engine parameters.
Some mechanisms ruling the engine and the combustion process
are in fact unknown and/or show hard nonlinearities. These diffi-
culties limit the effectiveness of traditional control approaches. In
this paper, we suggest a neural based solution to the air-to-fuel ratio
control in fuel injection systems. An indirect control approach has
been considered which requires a preliminary modeling of the en-
gine dynamics. The model for the engine and the final controller
are based on recurrent neural networks with external feedbacks.
Requirements for feasible control actions and the static precision
of control have been integrated in the controller design to guide

  

Source: Alippi, Cesare - Dipartimento di Elettronica e Informazione, Politecnico di Milano

 

Collections: Computer Technologies and Information Sciences