Sampled-data state estimation for a class of delayed complex networks via intermittent transmission

Abstract

This paper investigates the sampled-data state estimation problem for a class of delayed complex networks. At certain sampling times, transmission of sampled-data through communication network may fail, which means the considered estimator can only intermittently receive sampled-data. The main objective of this paper is to design a sampled-data state estimator subjected to intermittent transmission such that the error system is exponentially stable. Specifically, the error system is first transformed into time-varying delayed switched systems, including both stable and unstable subsystems. And then, to analyze the stability of the error system, a modified Halanay inequality is presented. In view of the modified Halanay inequality and switched systems methodology, a sufficient condition for globally exponential stability of the error system is established. Meanwhile, the upper bound of transmission failure rate is given, which reflects to be closely related to sampling period and the upper bound of node delays. Furthermore, the desired estimator gain of each node is explicitly provided by solving a set of matrix inequalities. Finally, a numerical simulation is carried out to verify the effectiveness of the inferred results.