Collision‐Free Adaptive Formation Control of Multiple Unmanned Aerial Vehicles Using Prescribed‐Time Reinforcement Learning

This paper addresses the prescribed‐time optimized formation tracking control problem for unmanned aerial vehicles (UAVs) while considering collision avoidance. The UAV system is divided into two parts: The position subsystem and the attitude subsystem. To achieve prescribed‐time convergence with adjustable accuracy, we develop a novel time‐varying transformation function that incorporates both the specified convergence time and desired precision parameters. For collision‐free formation maintenance, a modified barrier Lyapunov function is constructed to simultaneously enforce performance constraints and prevent inter‐agent collisions. The control architecture is enhanced through an innovative reinforcement learning (RL) strategy, where a fuzzy logic system (FLS)‐based actor‐critic structure is employed to approximate the optimal control policy by solving the derived Hamilton‐Jacobi‐Bellman (HJB) equations. The proposed algorithms are validated by simulation.