Intelligent Control for Aerospace Engineers: A Novel Educational Framework

The integration of intelligent control techniques into aerospace engineering education remains a challenge. This paper presents a novel approach for teaching intelligent control specifically designed for aerospace engineers, bridging the gap between theoretical foundations and practical applications. The proposed framework encompasses a comprehensive curriculum covering model-based and model-free approaches, leveraging neural networks, reinforcement learning, and other computational intelligence techniques. It emphasizes hands-on experiences through simulation-based exercises, hardware-in-the-loop experiments, and design projects tailored to different aerospace vehicle categories, including multi-rotor UAVs, helicopters, fixed-wing aircraft, and Hypersonic Flight Vehicles. The framework also addresses assessment methods, industry collaborations, and case studies to enhance student learning outcomes.