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; Nipon Theera-Umpon

doi:10.3390/en10010131

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, Nipon Theera-Umpon

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

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.

Original language	English
Article number	131
Journal	Energies
Volume	10
Issue number	1
DOIs	https://doi.org/10.3390/en10010131
Publication status	Published - 2017

Keywords

Air/fuel ratio (AFR)
Artificial neural networks
Fault-tolerant control
Nonlinear dynamics
Spark-ignition (SI) engines

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "A soft sensor-based fault-tolerant control on the air fuel ratio of spark-ignition engines",

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 = "Air/fuel ratio (AFR), Artificial neural networks, Fault-tolerant control, Nonlinear dynamics, Spark-ignition (SI) engines",

author = "Zhai, {Yu Jia} and Yu, {Ding Li} and Qian, {Ke Jun} and Sanghyuk Lee and Nipon Theera-Umpon",

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journal = "Energies",

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AU - Zhai, Yu Jia

AU - Yu, Ding Li

AU - Qian, Ke Jun

AU - Lee, Sanghyuk

AU - Theera-Umpon, Nipon

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Y1 - 2017

N2 - 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.

AB - 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.

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KW - Artificial neural networks

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KW - Nonlinear dynamics

KW - Spark-ignition (SI) engines

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A soft sensor-based fault-tolerant control on the air fuel ratio of spark-ignition engines

Abstract

Keywords

UN SDGs

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