Fuzzy control has achieved numerous successful industrial applications. However, stability analysis for fuzzy control systems remains a difficult problem, and most of the critical comments on fuzzy control are due to the lack of a general method for its stability analysis. Although significant research efforts have been made in the literature, appropriate tools for this issue have yet to be fou...

Over the past two decades, there has been a rapidly growing interest in approximating a nonlinear system by a Takagi-Sugeno (T-S) fuzzy model (Takagi & Sugeno, 1985). In general, this model is represented by using a set of fuzzy rules to describe a global nonlinear system in terms of a set of local linear models which are smoothly connected by fuzzy membership functions. Based on the T-S fuzzy ...

In this pape, a class of nonlinear time-delay systems based on the Takagi-Sugeno (T-S)fuzzy model is defined. We investigate the delay-independent stability of this model. A model-based fuzzy stabilization design utilizing the concept of the so-called “parallel distributed compensation” (PDC) is employed. The main idea of the controller design is to derive each control rule to compensate each r...

is obvious, and then what always hold in the Mamdani's case is the equivalence (p 2 q ! r) = (p ! r) ^ (q ! r): (14) Consequently, if I is a Mamdani–Larsen's implication (8) never holds, but (14) is always true. IV. CONCLUSION After remembering the four main types of implications used in Fuzzy Logic, the equality [p ^ q ! r] = [(p ! r) _ (q ! r)] (*) is analyzed bot in complemented lattices and...

This paper is concerned with the stability analysis for T-S fuzzy control systems. By exploiting the property of the structure of fuzzy inference engine, an equivalence relation on index set of the product of fuzzy rule weights is defined. Further, a new stability criterion is proposed by using the equivalence relation, and formulated into progressively less conservative sets of linear matrix i...

Klaus S hmid and Volker Krebs Universität Karlsruhe (TH), Institut für Regelungsund Steuerungssysteme Kaiserstr. 12, D-76131 Karlsruhe, Germany e-mail: {s hmid, krebs} irs.ete .uni-karlsruhe.de Abstra t. A dynami fuzzy system is a mapping of fuzzy input values onto a fuzzy output value with a feedba k to the input. In this paper, we present a new rule-based inferen e method that an be used in d...

The problem of sparse fuzzy rule bases is introduced. Because of the high computational complexity of the original compositional rule of inference (CRI) method, it is strongly suggested that the number of rules in a final fuzzy knowledge base is drastically reduced. Various methods of analogical reasoning available in the literature are reviewed. The mapping style interpretation of fuzzy rules ...

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T–S) inference mechanism and a new extended Gram–Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction o...

For a fast evaluation of fuzzy rule based systems (FRBS's) some special fuzzy hardware is available, but in most of the realized applications only standard hardware or specific non fuzzy ASICs are used. In this paper we discuss some of the reasons of this gap and give hints for a fast realization of FRBS's especially on digital standard processors. For the realiza tion a fast algorithm with an ...