Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems

The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.

Automated summary

This article investigates the problem of event-triggered resilient filtering for Markov jump systems. The study uses hidden Markov model to characterize asynchronous constraints between the filters and systems, and employs interval type of gain uncertainties for more accurate modeling. By separating the vertices of the uncertain interval, the number of linear matrix inequalities constraints can be significantly reduced, reducing the difficulty and time of calculation. Additionally, an event-triggered scheme is applied to minimize network resource consumption.