Feedback Error Learning Neural Networks for Air-to-Fuel Ratio Control in SI Engines 2003-01-0356
A controller is introduced for air-to-fuel ratio management, and the control scheme is based on the feedback error learning method. The controller consists of neural networks with linear feedback controller. The neural networks are radial basis function network (RBFN) that are trained by using the feedback error learning method, and the air-to-fuel ratio is measured from the wide-band oxygen sensor. Because the RBFNs are trained by online manner, the controller has adaptation capability, accordingly do not require the calibration effort. The performance of the controller is examined through experiments in transient operation with the engine-dynamometer.
Citation: Park, S., Yoon, M., and Sunwoo, M., "Feedback Error Learning Neural Networks for Air-to-Fuel Ratio Control in SI Engines," SAE Technical Paper 2003-01-0356, 2003, https://doi.org/10.4271/2003-01-0356. Download Citation
Author(s):
Seungbum Park, Maru Yoon, Myoungho Sunwoo
Affiliated:
Hanyang University
Pages: 5
Event:
SAE 2003 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Electronic Engine Control Technologies, 2nd Edition-PT-110, Electronic Engine Controls 2003-SP-1749, SAE 2003 Transactions Journal of Engines-V112-3
Related Topics:
Neural networks
Education and training
Sensors and actuators
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