Flocking of quad-rotor UAVs with fuzzy control

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Quad-rotor unmanned aerial vehicles (UAVs) present unique challenges for coordinated flight due to their complex dynamics.
• Existing flocking algorithms often simplify UAVs as mass points, neglecting crucial aspects like collision avoidance and spatial configuration.

Purpose of the Study:

• To develop a robust flocking algorithm for quad-rotor UAVs that accounts for realistic flight dynamics and optimizes airspace usage.
• To address and mitigate collision risks among UAVs during flocking maneuvers.
• To introduce a compact flight formation for enhanced spatial efficiency.

Main Methods:

• Derivation of a simplified, yet more realistic, quad-rotor UAV model.
• Development of a Takagi-Sugeno (T-S) fuzzy model for attitude dynamics and a corresponding T-S fuzzy feedback controller.
• Implementation of a double-loop control structure for attitude and position control.
• Proposal of a novel flocking algorithm incorporating collision avoidance and a quasi e-lattice formation.

Main Results:

• The proposed T-S fuzzy controller effectively manages quad-rotor attitude and position.
• The novel flocking algorithm successfully achieves coordinated flight while preventing collisions.
• The quasi e-lattice formation demonstrates improved airspace utilization for compact UAV formations.
• Numerical simulations validate the theoretical findings and the effectiveness of the proposed methods.

Conclusions:

• The developed T-S fuzzy control and flocking algorithm provide a viable solution for coordinated quad-rotor UAV operations.
• The approach enhances safety through collision avoidance and efficiency via compact formations.
• This research contributes to the advancement of multi-UAV systems for complex aerial tasks.