Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

Abstract

In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

Keywords:

• Time-varying systems
• event-triggered mechanism
• resilient filtering
• stochastic uncertainties
• successive packet dropouts

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 61673141], [grant number 11301118]; the Fok Ying Tung Education Foundation of China [grant number 151004]; the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province [grant number UNPYSCT-2016029]; the Postdoctoral Scientific Research Developmental Found of Heilongjiang Province of China [grant number LBH-Q16120]; the Science Funds for the Young Innovative Talents of HUST [grant number 201508]; the Alexander von Humboldt Foundation of Germany.