This paper proposes the design of a robust fault estimator for a class of nonlinear uncertain NCSs that ensures the fault estimation error is less than prescribed H performance level, irrespective of the uncertainties and networkinduced effects. T-S fuzzy models are firstly employed to describe the nonlinear plant. Markov processes are used to model these random network-induced effects. Suffici...

This paper presents a new approach for the stability analysis of TakagiSugeno (T-S) fuzzy systems. An idea is investigated to use piecewise quadratic Lyapunov function with low amount of computations. This class of Lyapunov function candidates is much richer than the common quadratic Lyapunov function. By exploiting the piecewise continuous Lyapunov function, we derive stability conditions that...

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...

The problem of robustly stabilising a class of nonlinear systems using an H1 fuzzy observer-based controller is considered. A class of nonlinear systems is approximated by a Takagi–Sugeno fuzzy model. A sufficient condition is derived to ensure the robust stability of the fuzzy observer-based controller with guaranteed H1 disturbance attenuation level. The observer and controller design problem...

We propose a new cryptographic notion called generalized closest substring encryption. In this notion, a ciphertext encrypted with a string S can be decrypted with a private key of another string S′, if there exist a substring of S, i.e. Ŝ, and a substring of S′, i.e. Ŝ′, that are “close” to each other measured by their “overlap distance”. The overlap distance between Ŝ and Ŝ′ is the number of ...

in this paper, we show that the chapman-kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a markov bilinear model. the stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.

In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.