Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system

Lifan Li; Chunhui Lei; Lina Yao; Jinglin Zhou; Hong Wang

doi:10.23919/ACC.2018.8431786

Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system

Lifan Li, Chunhui Lei, Lina Yao, Jinglin Zhou, Hong Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, a new fault diagnosis and fault tolerant control (FTC) algorithm is presented for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system based on fuzzy modeling. Linear fuzzy logic models are used to approximate the output probability density function (PDF) and Takagi-Sugeno fuzzy models are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input. Fault diagnosis is based on the use of a fuzzy fault diagnosis observer, with which the fault can be diagnosed. Based on the estimated fault and the desired PDF information, a fuzzy fault tolerant controller is designed to make the postfault PDF still track the given distribution. At last, simulation results on a flame shape distribution control system is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.

Original language	English
Title of host publication	2018 Annual American Control Conference, ACC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5781-5786
Number of pages	6
ISBN (Print)	9781538654286
DOIs	https://doi.org/10.23919/ACC.2018.8431786
State	Published - Aug 9 2018
Externally published	Yes
Event	2018 Annual American Control Conference, ACC 2018 - Milwauke, United States Duration: Jun 27 2018 → Jun 29 2018

Publication series

Name	Proceedings of the American Control Conference
Volume	2018-June
ISSN (Print)	0743-1619

Conference

Conference	2018 Annual American Control Conference, ACC 2018
Country/Territory	United States
City	Milwauke
Period	06/27/18 → 06/29/18

Funding

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (No. 61374128) and State Key Laboratory of Synthetical Automation for Process Industries and Henan Province University Innovation Talents Support Program (14HASTIT040). ∗This work was supported by the National Natural Science Foundation of China (No. 61374128) and State Key Laboratory of Synthetical Automation for Process Industries and Henan Province University Innovation Talents Support Program (14HASTIT040).

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10.23919/ACC.2018.8431786

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Li, L., Lei, C., Yao, L., Zhou, J., & Wang, H. (2018). Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system. In 2018 Annual American Control Conference, ACC 2018 (pp. 5781-5786). Article 8431786 (Proceedings of the American Control Conference; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8431786

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title = "Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system",

abstract = "In this paper, a new fault diagnosis and fault tolerant control (FTC) algorithm is presented for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system based on fuzzy modeling. Linear fuzzy logic models are used to approximate the output probability density function (PDF) and Takagi-Sugeno fuzzy models are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input. Fault diagnosis is based on the use of a fuzzy fault diagnosis observer, with which the fault can be diagnosed. Based on the estimated fault and the desired PDF information, a fuzzy fault tolerant controller is designed to make the postfault PDF still track the given distribution. At last, simulation results on a flame shape distribution control system is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.",

author = "Lifan Li and Chunhui Lei and Lina Yao and Jinglin Zhou and Hong Wang",

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Li, L, Lei, C, Yao, L, Zhou, J & Wang, H 2018, Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system. in 2018 Annual American Control Conference, ACC 2018., 8431786, Proceedings of the American Control Conference, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 5781-5786, 2018 Annual American Control Conference, ACC 2018, Milwauke, United States, 06/27/18. https://doi.org/10.23919/ACC.2018.8431786

Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system. / Li, Lifan; Lei, Chunhui; Yao, Lina et al.
2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 5781-5786 8431786 (Proceedings of the American Control Conference; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - In this paper, a new fault diagnosis and fault tolerant control (FTC) algorithm is presented for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system based on fuzzy modeling. Linear fuzzy logic models are used to approximate the output probability density function (PDF) and Takagi-Sugeno fuzzy models are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input. Fault diagnosis is based on the use of a fuzzy fault diagnosis observer, with which the fault can be diagnosed. Based on the estimated fault and the desired PDF information, a fuzzy fault tolerant controller is designed to make the postfault PDF still track the given distribution. At last, simulation results on a flame shape distribution control system is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.

AB - In this paper, a new fault diagnosis and fault tolerant control (FTC) algorithm is presented for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system based on fuzzy modeling. Linear fuzzy logic models are used to approximate the output probability density function (PDF) and Takagi-Sugeno fuzzy models are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input. Fault diagnosis is based on the use of a fuzzy fault diagnosis observer, with which the fault can be diagnosed. Based on the estimated fault and the desired PDF information, a fuzzy fault tolerant controller is designed to make the postfault PDF still track the given distribution. At last, simulation results on a flame shape distribution control system is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.

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Li L, Lei C, Yao L, Zhou J, Wang H. Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system. In 2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 5781-5786. 8431786. (Proceedings of the American Control Conference). doi: 10.23919/ACC.2018.8431786

Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system

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