Event-triggered Resilient Filtering for Time-varying Systems with Randomly Occurring Nonlinearity and Missing Measurements

This paper studies the event-triggered resilient filtering problem for a class of nonlinear systems with randomly occurring nonlinearity and missing measurements. Both the phenomena of the randomly occurring nonlinearity and the missing measurements are described by Bernoulli distributed random variables, where the occurrence probabilities could be uncertain. The event-triggered communication mechanism is introduced to save the network bandwidth during the data transmissions through the network. Additionally, the filter gain perturbations are characterized by employing the norm bounded uncertainties. The aim of the paper is to develop a robust event-triggered resilient filtering algorithm against the randomly occurring nonlinearity and missing measurements. Note that the analytical expressions of the filtering error covariance cannot be computed directly. Consequently, we derive its upper bound as an alternative way and subsequently minimize such an upper bound by properly designing the filter gain at each time step. Finally, an illustrative example is presented to show the effectiveness of the provided filtering algorithm.