A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems

Observer-based indirect adaptive fuzzy control for SISO nonlinear systems with unknown gain sign

“…During the last decade, much progress has been made in the field of observer design, and many observer-based adaptive fuzzy control schemes have been proposed for uncertain nonlinear systems [20,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Among them [27, 31-33, 35, 38] are for SISO nonlinear systems, [20, 28-30, 34, 39, 42, 43] are for MIMO nonlinear systems, and [40,41] are for strick feedback nonlinear systems.…”

Signatures: Definitions, operators and applications to fuzzy modelling

“…Some of the well accepted modelling that deal with rule bases in fuzzy models concern the sparse hierarchical rule bases [23], the fuzzy rule interpolation [24][25][26][27][28][29], the algebraic and the mathematical analysis of the properties of rule bases [30,31], the manipulation of operators [32], the development of evolving fuzzy systems [33] or the symbolic representation of data in terms of fuzzy signatures [34]. The current approaches to the representation and learning of multi-level hierarchical fuzzy inference systems include the observer-based adaptive controllers developed from hierarchical fuzzy neural networks [35], the adaptive fuzzy controllers based on variable structure algorithms [36], fuzzy expert systems [37], the analysis of interpretability measures [38], the identification of linguistic fuzzy models [39], the modelling in the framework of function approximation problems [40], or the use of linguistic preferences and incomplete information in modelling based on multi-level hierarchical fuzzy inference systems [41].…”

“…This is a serious advantage with respect to the state-of-the-art on the representation and learning in multi-level hierarchical fuzzy inference systems [35][36][37][38][39][40][41] as it offers a transparent algorithmic complexity representation.…”

Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms

“…With such remarkable attributes, FLS have been widely and successfully applied to control [1], classification [2] and modeling problem and in a considerable number of applications [3]- [5]. A fuzzy model is a set of fuzzy rules and the associated membership functions (MFs) that maps inputs to outputs.…”