Dynamic event-based non-fragile state estimation for complex networks via partial nodes information

In this paper, the non-fragile state estimation problem is investigated for a class of continuous-time delayed complex networks. In the addressed complex network model, the outputs only from partial network nodes are used to fulfill the state estimation task. For improving the efficiency of resource utilization, a dynamic event-triggering mechanism is applied in the design of estimator, where an auxiliary time-varying parameter is introduced to dynamically modulate the triggering condition. Our intention is to obtain the gain parameters of the desired non-fragile state estimator, which can tolerate the norm-bounded gain perturbation. In virtue of a novel Lyapunov functional and matrix inequality technique, sufficient conditions are provided to ensure robustly exponential boundedness for estimation error dynamics, and gain matrices of the estimator are computed based on certain matrix inequalities. An illustrative simulation is presented to show the validity of the non-fragile estimator proposed. (c) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the non-fragile state estimation problem for a class of continuous-time delayed complex networks. A dynamic event-triggering mechanism is applied to improve resource utilization efficiency and gain matrices of the estimator are computed based on certain matrix inequalities to ensure robustly exponential boundedness for estimation error dynamics. An illustrative simulation is presented to demonstrate the validity of the proposed non-fragile estimator.