Rendering compact formation and trajectory tracking for cyber unmanned ground vehicles

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.