In the design of modern and classical control systems, the first step is establish a suitable mathematical model to describe the behavior of the controlled plant (Takagi & Sugeno, 1985; Ying et al., 1990). However, in practical situations, such a requirement is not feasible because in practical control systems the plants are always nonlinear systems, which makes this task analytically unfeasibl...

Fuzzy set theory and fuzzy logic are the convenient tools for handling uncertain, imprecise, or unmodeled data in intelligent decision-making systems. The utility of fuzzy logic in system controls domain is presented in the context of a mobile robot navigation control application. The Takagi-Sugeno controller is a fuzzy model capable of approximating a wide class of nonlinear systems by decompo...

A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers and controllers. However, results obtained for Takagi-Sugeno fuzzy models are in general not directly applicable to the original nonlinear system. In this paper, we investigate what conclusions can be d...

Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuuy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the vie...

Evolving fuzzy systems are data-driven fuzzy (rule-based) systems supporting an incremental model adaptation in dynamically changing environments; typically, such models are learned on a continuous stream of data in an online manner. This paper advocates the use of visualization techniques in order to help a user gain insight into the process of model evolution. More specifically, rule chains a...

Recently, we have proposed a novel intuitionistic fuzzy inference system (IFIS) of Takagi-Sugeno type which is based on Atanassov’s intuitionistic fuzzy sets (IF-sets). The IFIS represent a generalization of fuzzy inference systems (FISs). In this paper, we examine the possibilities of the adaptation of this class of systems. Gradient descent method and other special optimization methods are em...

Recently, the local stability and local stabilization problems have been investigated by several authors for nonlinear systems described by Takagi–Sugeno (T–S) fuzzy systems. In this paper, we study the local stabilization problem for discrete-time T–S fuzzy systems with magnitudeand energy-bounded disturbances. The control design problem is cast as a linear matrix inequality (LMI) optimization...

|An optimization technique is proposed to represent a class of nonlinear systems by a Takagi-Sugeno uncertain fuzzy model. Then a robust H1 quadratic stabilization problem to the uncertain fuzzy systems via static output feedback is investigated. It is proved that existence of a set of solvable BMI su ces to guarantee the quadratic stabilization of an uncertain fuzzy system in an H1 sense. An L...