A secure control learning framework for cyber-physical systems under sensor attacks

Yuanqiang Zhou, Kyriakos G. Vamvoudakis, Wassim M. Haddad, Zhong Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor attacks. Specifically, we use several observer-based estimators to detect the attacks while also introducing a threat detection level function. We then solve the underlying joint state estimation and attack mitigation problems by using a reinforcement learning algorithm. Finally, an illustrative numerical example is provided to show the efficacy of the proposed framework.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4280-4285
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period7/10/197/12/19

Keywords

  • Attack estimation
  • Cyber-physical security
  • Differential games
  • Mitigation
  • Reinforcement learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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