Event-Based State Estimation for Networked Singularly Perturbed Complex Networks

Abstract

This paper deals with the multievent-triggering-based state estimation for a class of discrete-time networked singularly perturbed complex networks (SPCNs). A small singularly perturbed scalar is adopted to establish a discrete-time SPCNs model. To reduce the communication burdens, the data transmission between the sensor and the estimator is managed by a multievent generator function. Depending on the singularly-perturbed-based Lyapunov theory, a sufficient condition is constructed to guarantee that the estimation error is exponentially ultimately bounded in the mean square. Finally, the validity of the developed result is demonstrated by a simulation example.