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Neural network-based practical prescribed-time bearing-constrained secure formation control for second-order

Chuanjun Peng1, Chuanhai Yang2, Qingshan Liu3

  • 1School of Mathematics, Southeast University, Nanjing, 210096, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a robust formation control for mobile robots against false data injection attacks. The proposed method ensures task completion within a set time, even with limited sensor data and communication range.

Keywords:
Distributed bearing-constrained controlFalse data injection attacksMulti-mobile robot systemsNeural network

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Area of Science:

  • Robotics
  • Control Systems
  • Cybersecurity

Background:

  • Multi-mobile robot systems face challenges in formation control, especially under cyberattacks.
  • False data injection attacks threaten system integrity and task accomplishment.
  • Existing methods may struggle with unknown dynamics and limited sensing capabilities.

Purpose of the Study:

  • To develop a bearing-constrained formation control strategy for nonlinear multi-mobile robot systems.
  • To address the impact of false data injection attacks on robot formations.
  • To achieve formation control within a prescribed time, even in GPS-denied environments.

Main Methods:

  • Utilizing radial basis function neural networks to approximate robot dynamics and attack models.
  • Designing a distributed control protocol based on practical prescribed-time convergence.
  • Employing onboard sensors for relative bearing and distance measurements.

Main Results:

  • The proposed control strategy effectively mitigates the adverse effects of false data injection attacks.
  • The system achieves formation tasks within the preset time, demonstrating prescribed-time convergence.
  • Simulations confirm the algorithm's practicality in dynamic sensing environments with communication range limitations.

Conclusions:

  • The developed bearing-constrained formation control is resilient to false data injection attacks.
  • Prescribed-time convergence is achieved, enhancing the reliability of multi-mobile robot systems.
  • The approach offers a practical solution for formation control in uncertain and dynamic environments.