Predictive Control for Air Fuel Ratio of Gasoline Engine Based on Neural Network

2016 ◽  
Author(s):  
Hou Zhi-Xiang
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Gasoline Engine ◽  
Fuel Ratio

Neural Network Based Model Predictive Control of Gasoline Engine Air-Fuel Ratio

1996 ◽  
Vol 29 (1) ◽  
pp. 7784-7789
Author(s):  
B. Lennox ◽  
G.A. Montague ◽  
A.M. Frith ◽  
A.J. Beaumont
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Gasoline Engine ◽  
Fuel Ratio

Research in Elman Neural Network for AFR Model of Automotive Engine

2011 ◽  
Vol 204-210 ◽  
pp. 755-759
Author(s):  
Yu Hong Bu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gasoline Engine ◽  
Model Identification ◽  
Ratio Model ◽  
Power Performance ◽  
Elman Neural Network ◽  
Automotive Engine ◽  
Engine Model ◽  
Fuel Ratio

Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. In the paper, at first, it has studied the Elman neural network (NN) simulation model of Air Fuel ratio physical model of automotive engine. Second, employing the SI-V8 in en-DYNA engine model as experimental device, the paper discussed the structure determination of Elman neural network; finally, it compared model identification performance between Elman and BP neural network. Experiment results show the generalization performance of neural network does not have a linear relationship to the neurons in hidden layer of Elman NN, and the air fuel ratio based on Elman neural network is better than the air fuel ratio model based on BP neural network. The average relative error of Elman NN air fuel ratio model is less than 0.5%, however, which of BP NN is more than 1%.

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