This paper focuses on the non-fragile guaranteed cost control problem for a class of T-S fuzzy time-varying delay systems with local bilinear models. The objective is to design a non-fragile guaranteed cost state feedback controller via the parallel distributed compensation (PDC) approach such that the closed-loop system is delay-dependent asymptotically stable and the closed-loop performance i...

project participants are becoming more aware of the high costs and risks associated with delay claims and their litigation. among delays, weather delay has an important role in projects performed in severe environmental conditions. this research is the extension of delay analysis techniques by approving analysis of weather delays using fuzzy logic. at the presented technique, first using a fuzz...

Based on T-S fuzzy model, the guaranteed cost control problem of a class of uncertain nonlinear systems with input and state time delay is discussed. By constructing state-feedback controller, a sufficient condition that for the given performance index and control law, the closed-loop system is quadratic guaranteed cost stable is presented and expressed in terms of linear matrix inequality (LMI...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

This paper deals with control design and stability analysis for a class of nonlinear systems with system uncertainties as well as input constraint when using the T-S fuzzy model. As a result, it achieves an adaptive fuzzy controller, which consists of two components: one is corresponding with the regular state feedback controller based on the LMI approach in order to deal with the known part in...

In recent years, the Takagi–Sugeno (T–S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) stru...