Event-Triggered Least Squares Fault Estimation with Stochastic Nonlinearities

In this paper, the event-triggered least squares state and fault estimation problem is investigated for a class of systems with stochastic nonlinearities. An event-triggered scheme is properly proposed whose main idea is to transmit the measurement output to a remote estimator only when a specified event condition is violated and an event is triggered. A filter is designed so as to minimize an upper bound of the filtering error covariance with event-triggered measurement transmissions and additive stochastic nonlinearities. By solving two sets of discrete matrix equations, the desired filter parameters are calculated recursively and thus the method is applicable for online computation. Both the state and fault estimation problems are handled within the same framework using the least squares method. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.