In this paper, we address the asymptotic stability problem for the fractional-order neural networks system with uncertainty based on sampled-data control. First, considering the influence of uncertainty and fractional-order on the system, a new sampled-data control scheme with variable sampling period is designed. According to the input delay approach, the dynamics of the considered fractional-order system is modeled by a delay system. The main purpose of the problem addressed is to design a sampled-data controller, such that the closed-loop fractional-order system can guarantee the asymptotic stability. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are used to derive the stable conditions. The new delay-dependent and order-dependent stability conditions are presented in the form of LMIs. Furthermore, the sampling controller can be obtained to ensure the stability and stabilization of fractional-order system. Finally, a numerical example is given to demonstrate the effectiveness and advantages of the proposed method.

This paper investigates the problem of stability and stabilization for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered scheme (AETS) based on sampled-data control. AETS is used to relieve network congestion and save bandwidth resources. A novel Lyapunov–Krosovskii functional (LKF) is proposed by introducing the available information of fuzzy membership functions (FMFs) and sampling instant. The FMFs approach, extended reciprocal convex inequality technique and some slack matrices are fully utilized to deal with the derivative of the LKF. Then, an improved criterion with less conservatism is obtained to guarantee the stability of T-S fuzzy system. Moreover, the standard conditions are given in the form of linear matrix inequalities by the matrix decoupling technique. Finally, the feasibility and effectiveness of the proposed method can be demonstrated through a numerical simulation.