Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
Dynamic parallel distributed compensation for Takagi-Sugeno fuzzy systems: An LMI approach
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Constrained fuzzy controller design of discrete Takagi-Sugeno fuzzy models
Fuzzy Sets and Systems - Theme: Fuzzy control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Switching control of an R/C hovercraft: stabilization and smoothswitching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A relaxed stability criterion for T-S fuzzy discrete systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust H∞ disturbance attenuation for a class of uncertain discrete-time fuzzy systems
IEEE Transactions on Fuzzy Systems
Explicit controller formulas for LMI-based H∞ synthesis
Automatica (Journal of IFAC)
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper proposes a Takagi-Sugeno (T-S) fuzzy region model to relax the original one. Such switching concept has got rid of the complicated analysis of parallel distributed compensation (PDC). By mixing genetic algorithm (GA) and linear matrix inequality (LMI), we present a new hybrid approach about the static output feedback controller design. It is unlike other researches that involve abstruse mathematic transformations and system constraints that are difficult to find. In this paper, we fix the static output feedback gains by GA to solve the non-convex problem. It is proved that the existence of a set of solvable non-linear matrix inequality (NLMI) suffices to guarantee the stabilization of T-S fuzzy region system in H"~ sense. Numerical examples are given to illustrate the effectiveness of the algorithm and validate the new method.