Subspace-Based Takagi-Sugeno Modeling for Improved LMI Performance
"© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." ; [EN] Given a nonlinear system, the sector-nonlinearity methodology provides a systematic way of transforming it in an equivalent Takagi-Sugeno (T-S) model. However, such transformation is not unique: conservatism of shape-independent performance conditions in the form of linear matrix inequalities results in some models yielding better results than others. This paper provides some guidelines on choosing a sector-nonlinearity T-S model, with provable optimality (in a particular sense) in the case of quadratic nonlinearities. The approach is based on Hessian and restrictions of a function onto a subspace. ; This work was supported by the following institutions: Project Ciencia Basica SEP-CONACYT CB-168406, Project DPI2016-81002, (Spanish government, MINECO), Grant PROMETEOII/2013/004 (Generalitat Valenciana) and, the Scholarship GRISOLIA/2014/006. ; Robles-Ruiz, R.; Sala, A.; Bernal Reza, MÁ.; Gonzalez-German, IT. (2017). Subspace-Based Takagi-Sugeno Modeling for Improved LMI Performance. IEEE Transactions on Fuzzy Systems. 25(4):754-767. https://doi.org/10.1109/TFUZZ.2016.2574927 ; S ; 754 ; 767 ; 25 ; 4