Nonlinear Recurrent Neural Networks for Air Fuel Ratio Control in SI Engines Department of Mechanical Engineering, University of Salerno

Format:

Conference/Event

Author/Creator:

Arsie, Ivan, author.

Conference Name:

SAE 2004 World Congress & Exhibition (2004-03-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2004

Summary:

The paper deals with the use of Recurrent Neural Networks (RNNs) for the Air-Fuel Ratio (AFR) control in Spark Ignition (SI) engines. Because of their features, Neural Networks can perform an adaptive control more efficiently than classical techniques. In the paper, a review of the most useful control schemes based on Neural Networks is presented and the potential use in the field of engine control is analyzed.A preliminary controller has been implemented making use of a Direct Inverse Modeling approach. The controller compensates for the wall wetting dynamics and estimates the right amount of fuel to be injected to meet the target AFR during engine transients.The Direct Inverse Controller has been tested within an engine/vehicle simulator. The simulation tests have been performed by imposing a set of throttle transients at different engine speeds. The results show that the Inverse Model can satisfactorily bound the AFR excursions around the target value

Notes:

Vendor supplied data

Publisher Number:

2004-01-1364

Access Restriction:

Restricted for use by site license