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Reduced-Complexity Affine Representation for Takagi-Sugeno Fuzzy Systems

Abstract : This paper presents a systematic approach to reduce the complexity of sector nonlinearity TS fuzzy models using existing linear dependencies between local linear submodels. The proposed approach results in a decrease of the fuzzy model rules from 2P to p + 1 rules while maintaining equivalence to the TS fuzzy model. An LMI formulation is presented to obtain conditions for stability analysis and stabilizing controllers design with some examples to offer a comparison between the two models. The main purpose of reduced-complexity models is to keep the design and the structure of the nonlinear control and observer schemes as simple as possible for real-time implementation, especially when dealing with highly nonlinear systems with a very large number of premise variables. Two real-world robotics examples are provided to highlight the interests and the curent limitations of the proposed approach.
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Conference papers
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https://hal-uphf.archives-ouvertes.fr/hal-03405660
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Wednesday, October 27, 2021 - 1:41:39 PM
Last modification on : Wednesday, November 10, 2021 - 9:30:02 AM

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  • HAL Id : hal-03405660, version 1

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Amine Dehak, Tran Anh-Tu Nguyen, Antoine Dequidt, Laurent Vermeiren, Michel Dambrine. Reduced-Complexity Affine Representation for Takagi-Sugeno Fuzzy Systems. 21st IFAC World Congress, Jul 2020, Berlin, Germany. pp.8031-8036. ⟨hal-03405660⟩

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