This paper is mainly concerned with quantized stabilization for Takagi-Sugeno (T-S) fuzzy systems with hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme which is described by Bernoulli variable is adopted to mitigate the burden of the network. By taking the effect of hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality (LMI) techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.

This article deals with H∞ state estimation of neural networks with mixed delays. In order to make full use of delay information, novel delay-product Lyapunov-Krasovskii functional (LKF) by using parameterized delay interval is first constructed. Then, generalized free-weighting-matrix integral inequality is used to estimate the derivative of LKF to reduce the conservatism. Also, a more general activation function is further applied by combining with parameterized delay interval in order to obtain a more accurate estimator model. Finally, sufficient conditions are derived to confirm that the estimation error system is asymptotically stable with a prescribed H∞ performance. Numerical examples are simulated to show the benefits of our proposed method.