Adaptive Fuzzy Boundary Observer Design for Uncertain Linear Coupled Hyperbolic Partial Differential Equation Systems

Joint uncertainties and state estimation of a class of linear coupled hyperbolic partial differential equation systems in the presence of unstructured and structured uncertainties are studied in this paper. For unstructured uncertainties which are completely unknown, by employing Takagi-Sugeno fuzzy...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 31; číslo 10; s. 1 - 10
Hlavní autoři: Teng, Linbin, Yuan, Yuan, Luo, Biao, Yang, Chunhua, Dubljevic, Stevan, Huang, Tingwen, Xu, Xiaodong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive fuzzy observer
Adaptive systems
Backstepping
Distributed parameter system
Errors
Estimation error
Exponential convergence
Fuzzy logic
Fuzzy systems
Observers
Partial differential equations
State estimation
Swapping filters
Uncertainty
ISSN:1063-6706, 1941-0034
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Shrnutí:Joint uncertainties and state estimation of a class of linear coupled hyperbolic partial differential equation systems in the presence of unstructured and structured uncertainties are studied in this paper. For unstructured uncertainties which are completely unknown, by employing Takagi-Sugeno fuzzy logic system to approximate the unstructured uncertainties, a novel adaptive fuzzy boundary observer is developed to estimate both unknown system states as well as unknown weights in the fuzzy logic system, and the estimation errors are ultimately bounded. Therein, in the design of the proposed observer, a set of swapping filters and infinite dimensional backstepping technique are combined. On the other hand, for structured uncertainties that can be described in a concrete parameterized form, the proposed method can easily achieve the exact estimation of weights and states to their true values. The rigorous proof is provided to show that the ultimately bounded estimation errors for the case of unstructured uncertainties and the exponential convergent estimation errors for the case of structured uncertainties can be realized. Finally, three illustrative simulations are carried out to show the feasibility and effectiveness of the developed methods in this paper.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2023.3261284