Resilient state estimation for time-varying uncertain dynamical networks with data packet dropouts and switching topology: an event-triggered method

In this study, the recursive state estimation method based on the event-triggered protocol is given for time-varying uncertain complex networks with switching topology (ST), data packet dropouts (DPDs), and randomly occurring non-linearities (RONs). The event-triggered strategy is given to regulate the communications and then save the limited network resources when the measurement information is transmitted. The phenomena of RONs, ST, and DPDs are depicted by some random variables obeying the Bernoulli distribution. Besides, the estimator gain perturbations are dealt with. The major aim of this study is to present a new state estimation algorithm for addressed time-varying uncertain complex networks such that, for all STs, DPDs, and RONs, a minimal upper bound of state estimation error covariance matrix is derived by designing an optimal estimator gain matrix. Finally, a numerical simulation is presented to show the effectiveness of the provided time-varying state estimation method.