Flight Control

Flight control research aims to develop robust and adaptable systems for precise and safe aerial maneuvers, addressing challenges posed by unpredictable environmental factors and complex vehicle dynamics. Current efforts focus on integrating machine learning techniques, such as deep reinforcement learning (particularly Proximal Policy Optimization and Soft Actor-Critic) and Gaussian processes, with model-based control methods like model predictive control and PID controllers, to achieve real-time adaptation and improved performance in diverse scenarios. These advancements are crucial for enhancing the autonomy, safety, and efficiency of unmanned aerial vehicles (UAVs) across various applications, from autonomous delivery to precision agriculture and infrastructure inspection.