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Resilient \(H_{\infty }\) Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings

  • Xiangli Jiang
  • Guihua Xia
  • Zhiguang FengEmail author
Article
  • 14 Downloads

Abstract

In this paper, the resilient \(H_{\infty }\) filtering problem for a class of stochastically uncertain discrete-time systems with randomly occurring gain variations, nonlinearities (RONs) and channel fadings is investigated. Considering the existences of random parameter fluctuations in both actual plant and filter, a sequence of mutually independent random variables with known probabilistic distribution is utilized to better describe the stochastic characteristics. The phenomenon RONs, which is regulated by random variables with Bernoulli distribution, is used to reflect the reality that actual systems are generally nonlinear. The system measurement with channel fadings is described by the stochastic Rice fading model, where the channel coefficients are a set of mutually independent random variables with any probability density function. It is worthwhile to mention that a series of slack matrix variables are exploited to eliminate the coupling between the plant matrices and the designed filter parameters by providing free dimensions in the solution space. The purpose of this paper is to design a resilient \(H_{\infty }\) filter such that the filtering error system is asymptotically stable with a prescribed \(H_{\infty }\) performance level. And sufficient conditions for filter design are eventually converted into solving a convex optimization problem over linear matrix inequalities. A numerical example is presented to illustrate effectiveness of the proposed approach.

Keywords

Channel fadings Linear matrix inequalities Randomly occurring gain variations Randomly occurring nonlinearities Resilient \(H_{\infty }\) filtering 

Notes

Acknowledgements

The authors wish to thank the Associate Editor and the anonymous reviewers for their valuable suggestions and comments. This work was supported in part by the National Natural Science Foundation of China and China Academy of Engineering Physics [Grant Number U1530119], in part by the National Natural Science Foundation of China [Grant Number 61741305], in part by the Fundamental Research Funds for the Central Universities [Grant Number HEUCFJ190401], and in part by the China Postdoctoral Science Foundation [Grant Number 2018M63034, 2018T110275].

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of AutomationHarbin Engineering UniversityHarbinPeople’s Republic of China

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