A Soft Sensor-Based Fault-Tolerant Control on the Air Fuel Ratio of Spark-Ignition Engines

Yu-Jia Zhai, Ding-Li Yu, Ke-Jun Qian, Sanghyuk Lee and Nipon Theera-Umpon
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Yu-Jia Zhai: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 111, Ren’ai Road Duzhu Lake Higher Education Town SIP, Suzhou 215123, China
Ding-Li Yu: Control Research Group, Liverpool John Moores University, Liverpool L3 5UA, UK
Ke-Jun Qian: Suzhou Power Supply Company, Suzhou 215000, China
Sanghyuk Lee: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 111, Ren’ai Road Duzhu Lake Higher Education Town SIP, Suzhou 215123, China
Nipon Theera-Umpon: Biomedical Engineering Centre, Chiang Mai University, Chiang Mai 50200, Thailand

Energies, 2017, vol. 10, issue 1, 1-15

Abstract: The air/fuel ratio (AFR) regulation for spark-ignition (SI) engines has been an essential and challenging control problem for engineers in the automotive industry. The feed-forward and feedback scheme has been investigated in both academic research and industrial application. The aging effect can often cause an AFR sensor fault in the feedback loop, and the AFR control performance will degrade consequently. In this research, a new control scheme on AFR with fault-tolerance is proposed by using an artificial neural network model based on fault detection and compensation, which can provide the satisfactory AFR regulation performance at the stoichiometric value for the combustion process, given a certain level of misreading of the AFR sensor.

Keywords: spark-ignition (SI) engines; nonlinear dynamics; artificial neural networks; fault-tolerant control; air/fuel ratio (AFR) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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