Event-Based Robust State and Fault Estimation for Stochastic Linear System with Missing Observations and Uncertainty

This paper investigates event-based robust state and fault estimation problem for stochastic linear system with missing observations and norm-bounded uncertainty. Missing observations is depicted by Bernoulli distributed process. The measurements transmit to estimator unless the current innovation disobey the Send-on-Delta (SoD) conditions. Filtering algorithm is constructed by virtue of augmented state method combined with two-stage robust event-based estimators. Upper bounds of the estimation error covariance are obtained respectively according to solving discrete Riccati difference equations. Subsequently, by appropriately formulating the filter gain matrices such that the upper bounds are minimal. Simulation results demonstrate the usefulness of the algorithm.