Adaptive Autopilot

Adaptive autopilots aim to create autonomous systems capable of handling diverse and unpredictable situations, mimicking human-like decision-making and adapting to changing conditions. Current research emphasizes the use of deep reinforcement learning (DRL), particularly constrained DRL and model predictive control (MPC), alongside hybrid approaches combining model-based and machine learning components, to achieve robust and safe control. These advancements are crucial for improving the safety and reliability of autonomous vehicles, aircraft, and other robotic systems, addressing limitations of traditional fixed-gain controllers and paving the way for more sophisticated autonomous operations.