Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Quadrotor formations face challenges in obstacle avoidance and trajectory tracking due to inaccurate mathematical models.
• Traditional artificial potential field methods can get stuck in local optima, hindering effective path planning.

Purpose of the Study:

• To develop an advanced control strategy for quadrotor formations that addresses inaccurate modeling for robust obstacle avoidance and precise trajectory tracking.
• To overcome the local optimal problem inherent in artificial potential field methods.

Main Methods:

• Utilized the artificial potential field method with virtual force for obstacle avoidance path planning.
• Implemented an adaptive predefined-time sliding mode control algorithm based on Radial Basis Function (RBF) neural networks.
• Incorporated adaptive estimation of unknown interference within the quadrotor's mathematical model.

Main Results:

• The proposed algorithm successfully planned obstacle-avoiding trajectories for quadrotor formations.
• Quadrotor formations accurately tracked the planned trajectories within a predetermined time.
• The system adaptively estimated unknown interference, improving overall control performance.

Conclusions:

• The developed algorithm provides a robust solution for quadrotor formation control and obstacle avoidance under model uncertainties.
• Predefined-time convergence and adaptive interference estimation enhance navigation safety and efficiency.
• Theoretical derivations and simulations confirm the algorithm's effectiveness.