H_∞ control of memristive neural networks with aperiodic sampling and actuator saturation

Abstract

The H ∞ control problem for memristive neural networks with aperiodic sampling and actuator saturation is considered in this paper. A novel approach that is combined with the discrete-time Lyapunov theorem and sampled-data system is proposed to cope with the aperiodic sampling problem. On the basis of such method and choosing a polyhedral set, sufficient conditions to determine the ellipsoidal region of asymptotic stability and exponential stability for the estimation error system are obtained through a saturating sampled-data control. Furthermore, H ∞ performance index of memristive neural networks with disturbance is also analyzed, whereas the observer and controller gains are calculated from stability conditions of linear matrix inequalities. Finally, the effectiveness of the theoretical results is illustrated through the numerical examples.

Event‐triggered sampled‐data synchronization of complex networks with uncertain inner coupling and time‐varying delays

“…In [24], based on the SDC strategy, by constructing an improved time‐varying Lyapunov–Krasovskii functional (LKF) and using mathematical induction, synchronization criteria of complex networks were established. In [25], the author introduced multiple free weighting matrices to the LKF. Acyclic sampling is achieved through the constraint of the linear matrix inequality via inequality deflation, and the sampled‐data controller is designed to include an exponential decay term.…”