Order-Dependent Sampling Control of Uncertain Fractional-Order Neural Networks System

The asymptotic stability of the fractional-order neural networks system with uncertainty by sampled-data controller is addressed in the article. First, considering the influence of uncertainty and fractional-order on the system, a novel sampled-data controller is designed with alterable sampling period. In the light of the input delay approach, the fractional system is simulated by the delay system. The main purpose of the method presented is to design a sampled-data controller, which the closed-loop fractional-order system can guarantee the asymptotic stability. Then, the fractional-order Razumikhin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. The stability conditions are presented in the form of LMIs on the novel delay-dependent and order-dependent. Furthermore, the sampling controller can be acquired to promise the stability and stabilization for fractional-order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages for the provided method.