In this paper, stability analysis of the PDCbased Takagi-Sugeno (T-S) fuzzy impulsive control systems by using the extended Lyapunov stability theory is investigated. Some sufficient conditions and criterions are derived for stability of T-S fuzzy impulsive control system in terms of linear matrix inequalities (LMIs). Finally, control schemes are successfully applied to stabilize a predator-pre...

The main research intent of this paper is to introduce the use of fault tree analysis (FTA) and failure mode and effects analysis (FMEA) in conjunction to analyse the risk and reliability of a complex mechatronic system in both qualitative and quantitative manner. The major focus is on handling imprecise and vague information with the help of fuzzy synthesis of information. A complex mechatroni...

This article proposes a design method for producing H control performance for structural systems using the Tagagi-Sugeno (T-S) fuzzy model. A structural system with a tuned mass damper is modeled using a T-S type fuzzy model. Using the parallel distributed compensation (PDC) scheme, we design a nonlinear fuzzy controller for the tuned mass damper system. A sufficient stability condition for the...

The fuzzy integrals are a kind of fuzzy measures acting on fuzzy sets. They can be viewed as an average membershipvalue of fuzzy sets. The value of the fuzzy integral in a decision making environment where uncertainty is presenthas been well established. Most of the integral inequalities studied in the fuzzy integration context normally considerconditions such as monotonicity or comonotonicity....

This paper investigates the problem of passive delayed static output feedback control for a class of fuzzy systems. The system is described by a statespace Takagi–Sugeno (T-S) fuzzy model with additive delays and interval parameter uncertainties. The aim is to design a fuzzy delayed static output feedback controller which ensures the closed-loop system is passive for all admissible uncertaintie...

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

In this paper we investigate the System of fuzzy relation equations with inf → composition. We base on the knowledge of he system of fuzzy relation equations with sup−∗-composition. First, the general notions are explained and then are shown the smallest and maximal solutions. Finally is investigated a simple criterion of solvability for a special case of fuzzy sets.