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

Abstract

This paper focuses on the problem of event-triggered H∞ 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∞ disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example.