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

Xi'an Jiaotong-Liverpool University
Liverpool John Moores University
Chiang Mai University

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

10 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

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.3390/en10010131

Cite this

@article{a1fa78d939a54b42b7bce35a1f122c01,

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",

note = "Publisher Copyright: {\textcopyright} 2017 by the authors.",

year = "2017",

doi = "10.3390/en10010131",

language = "English",

volume = "10",

journal = "Energies",

issn = "1996-1073",

number = "1",

}

TY - JOUR

T1 - A soft sensor-based fault-tolerant control on the air fuel ratio of spark-ignition engines

AU - Zhai, Yu Jia

AU - Yu, Ding Li

AU - Qian, Ke Jun

AU - Lee, Sanghyuk

AU - Theera-Umpon, Nipon

PY - 2017

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.

KW - Air/fuel ratio (AFR)

KW - Artificial neural networks

KW - Fault-tolerant control

KW - Nonlinear dynamics

KW - Spark-ignition (SI) engines

UR - https://www.scopus.com/pages/publications/85022027280

U2 - 10.3390/en10010131

DO - 10.3390/en10010131

M3 - Article

AN - SCOPUS:85022027280

SN - 1996-1073

VL - 10

JO - Energies

JF - Energies

IS - 1

M1 - 131

ER -

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

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this