Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model unde...

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Veröffentlicht in:IEEE/CAA journal of automatica sinica Jg. 9; H. 6; S. 1026 - 1036
Hauptverfasser: Mao, Jingyang, Meng, Xiangyu, Ding, Derui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway Chinese Association of Automation (CAA) 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Department of Control Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China%Division of Electrical and Computer Engineering,Louisiana State University,Baton Rouge,LA,70803 USA %Department of Control Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
School of Science,Computing and Engineering Technologies,Swinburne University of Technology,Melbourne,VIC 3122,Australia
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ISSN:2329-9266, 2329-9274
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Abstract In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
AbstractList In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
Author Meng, Xiangyu
Mao, Jingyang
Ding, Derui
AuthorAffiliation Department of Control Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China%Division of Electrical and Computer Engineering,Louisiana State University,Baton Rouge,LA,70803 USA %Department of Control Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Science,Computing and Engineering Technologies,Swinburne University of Technology,Melbourne,VIC 3122,Australia
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School of Science,Computing and Engineering Technologies,Swinburne University of Technology,Melbourne,VIC 3122,Australia
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Snippet In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy...
In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy...
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SubjectTerms Affine model
Algorithms
Continuity (mathematics)
Discrete time systems
Ellipsoids
Estimation error
Filtering algorithms
Filtering theory
Filtration
Fuzzy sets
Information filters
Mathematical analysis
Mathematical models
membership functions
Noise measurement
Nonlinear systems
Optimization
set-membership filtering
stability
State estimation
Takagi-Sugeno fuzzy modeling
Title Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems
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