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Event-triggered interval estimation method for cyber-physical systems with unknown inputs.

Jun Huang1, Jianwei Fan1, Thach Ngoc Dinh2

  • 1The School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215131, China.

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|September 29, 2022
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Summary
This summary is machine-generated.

This paper presents secure estimation methods for cyber-physical systems facing unknown inputs and stealthy attacks. Novel techniques ensure system security and reliable state estimation under challenging conditions.

Keywords:
Cyber–physical systemsDescriptor systemsEvent-triggered observerUnknown inputs

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Area of Science:

  • Control Systems Engineering
  • Cybersecurity
  • Signal Processing

Background:

  • Cyber-physical systems (CPS) are vulnerable to unknown inputs and stealthy deception attacks, compromising secure state estimation.
  • Efficient data transmission is crucial in networked CPS to avoid communication burden and network congestion.

Purpose of the Study:

  • To develop robust and secure state estimation techniques for CPS under unknown inputs and deception attacks.
  • To introduce an event-triggered mechanism for efficient communication in state estimation.

Main Methods:

  • Unknown inputs are augmented as states, transforming the plant into a decoupled singular system.
  • An event-triggered mechanism is employed to reduce communication load.
  • Two state estimation approaches are presented: the monotone system method and a robust observer using set-membership for approximated error.

Main Results:

  • The proposed methods effectively decouple unknown inputs.
  • The event-triggered mechanism successfully reduces communication burden.
  • Both estimation approaches demonstrate effectiveness in handling system uncertainties and attacks.

Conclusions:

  • The developed secure estimation strategies enhance the resilience of cyber-physical systems.
  • The integration of event-triggered mechanisms offers an efficient solution for networked CPS state estimation.
  • The presented methods provide a robust framework for secure estimation in the presence of unknown inputs and stealthy attacks.