Distributed coordinated motion control of multiple UAVs oriented to optimization of air-ground relay network

Area of Science:

• Robotics and Control Systems
• Wireless Communications
• Network Engineering

Background:

• Coordinating multiple unmanned aerial vehicles (UAVs) for communication relay presents challenges due to unpredictable user mobility, environmental impacts on signal propagation, and limited location data.
• Existing methods often struggle to integrate these dynamic factors into effective motion control strategies for UAV relays.

Purpose of the Study:

• To develop a novel adaptive model-based motion control method for multi-UAV communication relay systems.
• To enhance network connectivity and communication performance for ground-based mobile networks.
• To address the complexities of UAV coordination, including unknown user mobility and environmental channel effects.

Main Methods:

• Utilized graph theory and minimum spanning trees to construct network connectivity and define a communication performance index.
• Implemented a multi-UAV motion control strategy combining Improved Particle Swarm Optimization (IPSO) and Distributed Nonlinear Model Predictive Control (DNMPC).
• Employed a Kalman filter for estimating future positions of mobile ground nodes.

Main Results:

• Simulations demonstrated the method's ability to guide UAVs to optimal relay positions or track them effectively.
• Significant improvements in network performance were observed in both simple and complex simulated environments.
• The study validated the importance of considering environmental impacts on channel characteristics for UAV relay control.

Conclusions:

• The proposed adaptive model-based motion control method successfully enhances multi-UAV communication relay performance.
• The integrated approach effectively manages complex variables like mobility, environmental factors, and coordination.
• This research offers a robust solution for improving wireless communication networks utilizing UAV relays.