Air/fuel ratio control in SI1 engines using a combined neural network and estimator

Abstract

In this paper by using a controller based on a neural network and an estimator, an efficient method in AIF ratio for SI engines is presented. This combined method improves plant performance effectively and provides robustness against disturbances due to work point changing. It will he shown that by combining two separate methods, a useful control strategy may be generated. Simulation results reveal the superiority of this method.

A ratio control scheme decoupling disturbance response from set-point response

“…For instance, fuzzy logic techniques, dynamic sliding mode control and neural network algorithm have been applied to the airfuel ratio control [8][9][10][11][12][13][14][15][16]. Although these methods contribute to improved ratio control performance, they are based on numerical calculation, and thus, it is time-consuming and troublesome for implementation.…”