A Fuzzy Neural Network and Application to Air-Fuel Ratio Control under Gasoline Engine Transient Condition

Author

Liu, Zhi-qiang ; Zhou, Yu-cai

Author_Institution

Sch. of Automotive Eng., Wuhan Univ. of Sci. & Technol., Wuhan, China

Volume

1

fYear

2010

fDate

13-14 Oct. 2010

Firstpage

24

Lastpage

26

Abstract

In the paper, a Hendricks Mean Value Engine Model is established by using SIMULINK. At the same time, a fuzzy neural network is designed. The AFR is simulated under transient conditions. The simulation result shows that: With no controller, when throttle degree is changed intensively, the AFR errors are large, With the FNN controller, the AFR errors can be controlled to a narrow range, and the system has shorter adjust-time, smaller overshoot. So, the fuzzy neural network controller has good control performance under gasoline engine transient condition.

Keywords

automotive engineering; fuel; fuzzy neural nets; internal combustion engines; neurocontrollers; value engineering; AFR error; FNN controller; Hendricks mean value engine model; SIMULINK; air fuel ratio control; fuzzy neural network; gasoline engine transient condition; throttle degree; Artificial neural networks; Atmospheric modeling; Automotive engineering; Engines; Fuzzy control; Fuzzy neural networks; Transient analysis; Air-Fuel Ratio; Fuzzy Neural; Gasoline Engine; Transient Condition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-8333-4

Type

conf

DOI

10.1109/ISDEA.2010.228

Filename

5743122