Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Convex Optimization
Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems
Fuzzy Sets and Systems
A fuzzy clustering algorithm enhancing local model interpretability
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Relaxed LMI conditions for closed-loop fuzzy systems with tensor-product structure
Engineering Applications of Artificial Intelligence
Guaranteed cost control of polynomial fuzzy systems via a sum of squares approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy filter design for itô stochastic systems with application to sensor fault detection
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust fuzzy control of nonlinear systems with parametric uncertainties
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Controller Design Under Fuzzy Pole-Placement Specifications: An Interval Arithmetic Approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Guaranteed cost control analysis and iterative design for constrained Takagi-Sugeno systems
Engineering Applications of Artificial Intelligence
IEEE Transactions on Fuzzy Systems
Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions
Fuzzy Sets and Systems
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
Information Sciences: an International Journal
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Classical Takagi-Sugeno (T-S) fuzzy models are formed by convex combinations of linear consequent local models. Such fuzzy models can be obtained from nonlinear first-principle equations by the well-known sector-nonlinearity modeling technique. This paper extends the sector-nonlinearity approach to the polynomial case. This way, generalized polynomial fuzzy models are obtained. The new class of models is polynomial, both in the membership functions and in the consequent models. Importantly, T-S models become a particular case of the proposed technique. Recent possibilities for stability analysis and controller synthesis are also discussed. A set of examples shows that polynomial modeling is able to reduce conservativeness with respect to standard TS approaches as the degrees of the involved polynomials increase.