Dynamic event-triggered H∞ state estimation for delayed complex networks with randomly occurring nonlinearities

This paper aims to iron out the dynamic event-triggered (ET) H. state estimation issue for a class of discrete time-delay complex networks (CNs) with randomly occurring nonlinearities (RONs). In the signal transmission among the nodes, the effect of time-varying delays is examined. The RONs under consideration is modelled by a series of random variables obeying Bernoulli distribution. In the design of state estimators, a dynamic ET scheme is utilized with the hope of improving the energy utilization efficiency. A sufficient condition is first derived to ensure the exponential mean-square (EMS) stability and H. performance index of the estimation error systems. Then, by using the matrix inequality technology, the desired state estimators are designed. Lastly, a numerical simulation example is given to show the usefulness of the proposed estimator design algorithm. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the dynamic event-triggered state estimation issue for a class of discrete time-delay complex networks with randomly occurring nonlinearities. By utilizing a dynamic ET scheme and matrix inequality technology, state estimators are designed to improve energy utilization efficiency and system stability. A numerical simulation example demonstrates the usefulness of the proposed estimator design algorithm.