Event-triggered Resilient Filtering for Time-varying Systems with Randomly Occurring Nonlinearity and Missing Measurements

Ming Gao, Jun Hu*, Hongxu Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper studies the event-triggered resilient filtering problem for a class of nonlinear systems with randomly occurring nonlinearity and missing measurements. Both the phenomena of the randomly occurring nonlinearity and the missing measurements are described by Bernoulli distributed random variables, where the occurrence probabilities could be uncertain. The event-triggered communication mechanism is introduced to save the network bandwidth during the data transmissions through the network. Additionally, the filter gain perturbations are characterized by employing the norm bounded uncertainties. The aim of the paper is to develop a robust event-triggered resilient filtering algorithm against the randomly occurring nonlinearity and missing measurements. Note that the analytical expressions of the filtering error covariance cannot be computed directly. Consequently, we derive its upper bound as an alternative way and subsequently minimize such an upper bound by properly designing the filter gain at each time step. Finally, an illustrative example is presented to show the effectiveness of the provided filtering algorithm.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4656-4661
Number of pages6
ISBN (Electronic)9781538612439
DOIs
Publication statusPublished - 6 Jul 2018
Event30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China
Duration: 9 Jun 201811 Jun 2018

Conference

Conference30th Chinese Control and Decision Conference, CCDC 2018
Country/TerritoryChina
CityShenyang
Period9/06/1811/06/18

Keywords

  • event-triggered mechanism
  • missing measurements
  • randomly occurring nonlinearity
  • Time-varying systems
  • uncertain occurrence probabilities

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