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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE/CAA journal of automatica sinica Ročník 9; číslo 6; s. 1026 - 1036
Hlavní autoři: Mao, Jingyang, Meng, Xiangyu, Ding, Derui
Médium: Journal Article
Jazyk:angličtina
Vydáno: 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
Témata:
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
membership functions
Affine model
Takagi-Sugeno fuzzy modeling
stability
set-membership filtering
ISSN:2329-9266, 2329-9274
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2022.105416