Event-Triggered Filtering for Delayed Markov Jump Nonlinear Systems with Unknown Probabilities

This paper focuses on the problem of event-triggered H-infinity asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Both the probability information involved in the original system and the filter are assumed to be only partly available. Under this framework, via employing the Lyapunov-Krasovskii functional and matrix inequality transformation techniques, a sufficient condition is given and the filter is further devised to ensure that the resulting filtering error dynamic system is stochastically stable with a desired H-infinity, disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example.