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Resilient event-triggering adaptive neural network control for networked systems under mixed cyber attacks.

Ning Zhao1, Dongke Zhao1, Yongchao Liu2

  • 1College of Control Science and Engineering, Bohai University, Jinzhou 121013, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 26, 2024
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Summary

This study introduces a new resilient event-triggered mechanism for networked control systems facing cyber attacks. The approach uses neural networks to defend against unknown attacks and ensure system stability, validated on a robot manipulator.

Keywords:
Event-triggered mechanismMixed cyber attacksNetworked control systemsNeural networks

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Area of Science:

  • Control Systems Engineering
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Networked control systems are vulnerable to mixed cyber attacks, including denial-of-service and deception attacks.
  • Conventional event-triggered mechanisms (ETMs) with constant thresholds are insufficient against sophisticated and state-dependent attacks.
  • Existing methods often struggle to address unknown, nonlinear attack signals that impact system states.

Purpose of the Study:

  • To develop a resilient event-triggering adaptive neural network (NN) control strategy for networked control systems under mixed cyber attacks.
  • To design a novel ETM capable of withstanding denial-of-service attacks and conserving communication resources.
  • To address unknown state-dependent nonlinear deception attacks using neural network approximation.

Main Methods:

  • A novel resilient event-triggered mechanism (ETM) is proposed, improving upon conventional constant-threshold ETMs.
  • Neural network (NN) techniques are employed to approximate unknown state-dependent nonlinear attack signals.
  • An adaptive controller is designed to counteract deception attacks, with system boundedness ensured by Lyapunov functional analysis.

Main Results:

  • The proposed resilient ETM effectively conserves communication resources while withstanding denial-of-service attacks.
  • The NN-based approach successfully identifies and compensates for unknown state-dependent deception attacks.
  • Sufficient conditions for system boundedness are derived, and a co-design strategy for control gain and event-triggering parameters is presented.

Conclusions:

  • The developed resilient event-triggering adaptive NN control strategy enhances the security and robustness of networked control systems against mixed cyber attacks.
  • The approach demonstrates effectiveness in handling unknown, state-dependent nonlinear attacks, a significant advancement over existing methods.
  • Validation on a robot manipulator system confirms the practical feasibility and performance of the proposed control strategy.