ABSTRACT
This chapter deals with the filtering and state estimation problems under dynamic event-triggered mechanisms. Firstly, the dynamic event-triggered filtering problem is investigated for a class of discrete time-varying systems with censored measurements and parameter uncertainties. By means of the mathematical induction, an upper bound is derived for the filtering error covariance in terms of recursive equations and such an upper bound is then minimized by designing the filter gain properly. Furthermore, the boundedness is discussed for the minimized upper bound of the filtering error covariance. Secondly, we consider the system outputs are collected through a sensor network subject to a time-varying topology that is connected via Gilbert-Elliott channels and governed by a set of Markov chains. By using similar analysis techniques, the corresponding dynamic event-triggered distributed filtering problem is studied. Thirdly, the finite-time resilient H∞ state estimation problem is discussed for delayed neural networks under dynamic event-triggered mechanisms. Lyapunov functional approach is carried out to obtain sufficient conditions for the existence of desired estimators ensuring both the finite-time boundedness and the H∞ performance of the estimation error system. Finally, some numerical examples are employed to illustrate the usefulness of the proposed design techniques in this chapter.
