Predefined-time formation control of UAV swarm under spatiotemporal constraints

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Artificial Intelligence

Background:

• Coordinated control of multiple unmanned aerial vehicles (UAVs) is crucial for complex missions.
• Existing formation control methods often lack predefined-time convergence or efficient obstacle avoidance.
• Spatiotemporal coupling constraints pose significant challenges in UAV formation tasks.

Purpose of the Study:

• To develop a novel predefined-time formation control strategy for UAVs.
• To design an intelligent collision prediction mechanism to minimize unnecessary obstacle avoidance.
• To address spatiotemporal coupling constraints in UAV formation control.

Main Methods:

• A predefined-time formation controller utilizing time-based generators and sliding mode control (SMC).
• An artificial potential field (APF) based collision prediction mechanism.
• Simulation analysis to validate the proposed control scheme.

Main Results:

• The proposed controller mitigates initial input saturation issues inherent in some control designs.
• The collision prediction mechanism effectively distinguishes between threatening and non-threatening obstacles, reducing redundant avoidance maneuvers.
• Simulations demonstrate successful predefined-time formation achievement and reconstruction post-obstacle avoidance.

Conclusions:

• The developed controller ensures UAVs achieve formation and maintain it within a specified time, even after encountering obstacles.
• The intelligent collision avoidance strategy enhances efficiency by avoiding unnecessary actions, conserving resources.
• The proposed approach effectively handles spatiotemporal coupling constraints in UAV formation control.