Air-to-fuel ratio control of spark ignition engines using Gaussian network sliding control

Abstract

This paper treats air-to-fuel ratio control of a spark ignition engine. A direct adaptive control method using Gaussian neural networks is developed to compensate transient fueling dynamics and the measurement bias of mass air flow rate into the manifold. The transient fueling compensation method is coupled with a dynamic sliding mode control technique that governs fueling rate when the throttle change is not rapid. The proposed controller is simple enough for online computation and is successfully implemented on an automotive engine having a multiport fuel injection system.