Intermittent dynamic event-triggered state estimation for delayed complex networks based on partial nodes

In this paper, the intermittent dynamic event-based state estimation problem is investigated for a class of delayed complex dynamic networks (CDNs). The estimate is implemented based on the measurements from a fraction of network nodes. In the framework of aperiodic intermittent measurement outputs, a dynamic event-triggered mechanism is introduced to save communication resources and reduce actua-tion burden. The aim of this work is to design a dynamic event-based state estimator by adopting inter-mittent dynamic event-triggered (IDET) strategy, such that the dynamics of estimation error system is exponentially stable. By resorting to Halanay inequality and switched system method, the sufficient con-ditions are derived for ensuring the existence of the desired state estimator, and are characterized by the ratio of the average working time to the average rest time. In the meanwhile, the estimator gains for par-tial nodes are explicitly obtained by solving some matrix inequalities. Furthermore, it is also proven that Zeno behavior can be excluded under the proposed IDET strategy. Finally, a numerical simulation is pro-vided to verify the effectiveness of the derived results. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the intermittent dynamic event-based state estimation problem for a class of delayed complex dynamic networks (CDNs). A dynamic event-triggered mechanism is introduced to save communication resources and achieve exponential stability of the estimation error system. The sufficient conditions for the existence of the desired state estimator are derived based on the ratio of average working time to average rest time, and explicit estimator gains for partial nodes are obtained through solving matrix inequalities. Furthermore, the proposed IDET strategy can exclude Zeno behavior, as proven in the study.