Order‐dependent sampling control for state estimation of uncertain fractional‐order neural networks system

Abstract

AbstractIn this paper, the problem of state estimation for a fractional‐order neural networks system with uncertainties is studied by a sampled‐data controller. First, considering the convenience of digital field, such as anti‐interference, not affected by noise, a novel sampled‐data controller is designed for the fractional‐order neural network system of uncertainties with changeable sampling time. In the light of the input delay approach, the sampled‐data control system of fractional‐order is simulated by the delay system. The main purpose of the presented method is to obtain a sampled‐data controller gain to estimate the state of neurons, which can guarantee the asymptotic stability of the closed‐loop fractional‐order system. Then, the fractional‐order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. Improved delay‐dependent and order‐dependent stability conditions are given in the form of LMIs. Furthermore, the sampled‐data 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 of the provided method.