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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Rendering compact formation and trajectory tracking for cyber unmanned ground vehicles.

Xiaomin Zhao1, Zhengrong Cui1, Chee-Meng Chew2

  • 1School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China.

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This study addresses cyber interference in unmanned ground vehicle swarm systems. Cooperative adaptive robust controls ensure stable, compact formations and trajectory tracking despite network security challenges.

Keywords:
ConstraintCooperative controlCyber interferenceSwarm systemUnmanned ground vehicle

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Area of Science:

  • Robotics and Control Systems
  • Cyber-Physical Systems
  • Networked Autonomous Systems

Background:

  • Unmanned ground vehicle (UGV) swarms require robust control strategies to manage complex behaviors.
  • Cyber interference and system uncertainties pose significant threats to the security and performance of networked swarm systems.
  • Ensuring reliable operation in the face of cyber-attacks is crucial for UGV swarm applications.

Purpose of the Study:

  • To develop a cooperative control framework for unmanned ground vehicle swarm systems facing cyber interference.
  • To design adaptive robust control strategies that account for system uncertainties and cyber threats.
  • To achieve global stability, compact formation, cooperative hunting, and trajectory tracking for the UGV swarm.

Main Methods:

  • Abstracting integrated potential functions to model swarm behaviors (attraction, repulsion, trajectory tracking) into a kinematic model.
  • Utilizing the kinematic model as a system constraint and decomposing system uncertainties.
  • Proposing a class of cooperative adaptive robust controls to ensure adherence to system constraints.

Main Results:

  • Demonstrated global stability, including uniform boundedness and uniform ultimate boundedness of the UGV swarm.
  • Achieved compact formation maintenance among the unmanned ground vehicles.
  • Successfully implemented cooperative hunting behaviors within the swarm.
  • Verified accurate trajectory tracking for the entire UGV system.

Conclusions:

  • The proposed cooperative adaptive robust control effectively manages cyber interference and system uncertainty in UGV swarms.
  • The developed control strategy ensures robust performance across multiple key swarm objectives: stability, formation, hunting, and tracking.
  • This research contributes a viable solution for secure and reliable cooperative control of intelligent autonomous vehicle systems.