Dynamic Event-Triggering Joint State and Unknown Input Estimation for Nonlinear Systems With Random Sensor Failure

In this article, the issue of the state and unknown input estimation (SUIE) is ironed out for nonlinear systems subject to random sensor failure (RSF) under the dynamic event-triggering mechanism (DETM). To save energy, DETM is used to regulate the frequency of data transmissions from the sensor to the estimator, in which each transmission is delivered only when the absolute error is more significant than a predefined threshold. Moreover, RSF is described by a random variable with a certain distribution function. The purpose of this article is to develop a joint state and unknown input estimator such that in the presence of RSF, DETM, and nonlinearities, the upper bounds (UBs) on the estimation error covariances (EECs) of the state and the unknown input are guaranteed and then minimized by parameterizing the estimator gain properly. In addition, a sufficient condition is constructed for the convergence of the designed estimator, and the monotonicity analysis is also established subsequently. Finally, comprehensive illustrative examples are used to illustrate the validity of the proposed estimation algorithm.