Quadrotor Control

Quadrotor control research focuses on developing algorithms that enable stable, agile, and efficient flight, often addressing challenges like high-speed maneuvers, obstacle avoidance, and robustness to disturbances. Current efforts concentrate on advanced control techniques such as model predictive control (MPC), reinforcement learning (RL), and hybrid approaches combining both, often employing neural networks for function approximation and data-driven system identification. These advancements are crucial for expanding the capabilities of autonomous aerial vehicles in diverse applications, including drone racing, search and rescue, and infrastructure inspection.