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 proceedingConference contributionpeer-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 languageEnglish
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5781-5786
Number of pages6
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Externally publishedYes
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Conference

Conference2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States
CityMilwauke
Period06/27/1806/29/18

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).

FundersFunder number
National Natural Science Foundation of China61374128
State Key Laboratory of Synthetical Automation for Process Industries14HASTIT040

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