Reduced-Complexity Affine Representation for Takagi-Sugeno Fuzzy Systems. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Dehak, Amine; Nguyen, Anh-Tu; Dequidt, Antoine; Vermeiren, Laurent; Dambrine, Michel
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 8031
• Page End: 8036
• Publication Date: 2020
• Subjects: Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2235
• Languages: English
• ISSNs: 2405-8963

Abstract

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 2 P 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.