Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

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4 Citations (Scopus)

Abstract

This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and
latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using moving horizon estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated moving horizon estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the moving horizon estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general moving horizon estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
Original languageEnglish
Title of host publicationProceedings of the 2016 American Control Conference (ACC 2016)
EditorsK. Johnson, G. Chiu, D. Abramovitch
Place of PublicationPiscataway, NY, USA
PublisherIEEE
Pages5969-5975
ISBN (Electronic)978-1-4673-8682-1
ISBN (Print)978-1-4673-8683-8
DOIs
Publication statusPublished - 2016
EventAmerican Control Conference (ACC), 2016 - Boston, MA, United States
Duration: 6 Jul 20168 Jul 2016

Conference

ConferenceAmerican Control Conference (ACC), 2016
Abbreviated titleACC 2016
CountryUnited States
CityBoston, MA
Period6/07/168/07/16

Keywords

  • aerospace control
  • aircraft
  • estimation theory
  • fault diagnosis
  • matrix algebra
  • robust control
  • Karush-Kuhn-Tucker conditions
  • active inequality constraints
  • constrained residual generator
  • enhanced fault sensitivity
  • high-fidelity Airbus simulator
  • moving horizon estimation based residual generator
  • robust air data sensor fault diagnosis
  • robustness to winds
  • weighting matrices
  • Atmospheric modeling
  • Estimation
  • Fault diagnosis
  • Generators
  • Redundancy
  • Robustness
  • Sensitivity

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