LAPSE:2023.4190
Published Article
LAPSE:2023.4190
T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor
February 22, 2023
Abstract
Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme.
Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme.
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Keywords
channel fading, continuous stirring reactors, Fault Detection, T-S fuzzy model
Subject
Suggested Citation
Wang Y, Ren W, Liu Z, Li J, Zhang D. T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor. (2023). LAPSE:2023.4190
Author Affiliations
Wang Y: School of Mechatronics Engineering, Daqing Normal University, Daqing 163712, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intellige
Ren W: Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China
Liu Z: School of Electrical Engineering, University of Rostock, 18051 Rostock, Germany
Li J: School of Mechatronics Engineering, Daqing Normal University, Daqing 163712, China
Zhang D: Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China
Ren W: Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China
Liu Z: School of Electrical Engineering, University of Rostock, 18051 Rostock, Germany
Li J: School of Mechatronics Engineering, Daqing Normal University, Daqing 163712, China
Zhang D: Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China
Journal Name
Processes
Volume
9
Issue
12
First Page
2127
Year
2021
Publication Date
2021-11-25
ISSN
2227-9717
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Original Submission
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PII: pr9122127, Publication Type: Journal Article
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LAPSE:2023.4190
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https://doi.org/10.3390/pr9122127
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[v1] (Original Submission)
Feb 22, 2023
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Feb 22, 2023
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