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An adaptive control architecture for cyber-physical system security in the face of sensor and actuator attacks and exogenous stochastic disturbances

  • Xu Jin
  • , Wassim M. Haddad
  • , Tomohisa Hayakawa

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

8 Scopus citations

Abstract

In this paper, we propose a novel adaptive control architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an adaptive controller for time-invariant, state-dependent adversarial sensor and actuator attacks in the face of stochastic exogenous disturbances. We show that the proposed controller guarantees uniform ultimate boundedness of the closed-loop dynamical system in a mean-square sense. We further discuss the practicality of the proposed approach and provide a numerical example involving the lateral directional dynamics of an aircraft to illustrate the efficacy of the proposed adaptive control architecture.

Original languageEnglish
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Pages1380-1385
Number of pages6
ISBN (Electronic)9781509028733
DOIs
StatePublished - Jun 28 2017
Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
Duration: Dec 12 2017Dec 15 2017

Publication series

Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume2018-January

Conference

Conference56th IEEE Annual Conference on Decision and Control, CDC 2017
Country/TerritoryAustralia
CityMelbourne
Period12/12/1712/15/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Funding

This work was supported in part by the Air Force Office of Scientific Research under Grant FA9550-16-1-0100.

FundersFunder number
Air Force Office of Scientific Research, United States Air ForceFA9550-16-1-0100
Air Force Office of Scientific Research, United States Air Force

    ASJC Scopus subject areas

    • Decision Sciences (miscellaneous)
    • Industrial and Manufacturing Engineering
    • Control and Optimization

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