Robust flight control of urban air mobility vehicles in icing conditions

The project aims to develop new flight control systems for Urban Air Mobility (UAM) vehicles, designed for short to medium-distance travel within cities. However, the operation of these vehicles, especially in Canada’s inclement weather conditions face significant challenges, particularly when flying in icy conditions, which can endanger flight safety by affecting the aircraft’s stability and control. To tackle this, the project will bring together researchers from South Korea and Canada to work on advanced flight control algorithms. The focus will be on two main technologies: Nonlinear Model Predictive Control (NMPC) and Deep Reinforcement Learning (DRL). NMPC is known for its ability to handle complex, uncertain conditions by predicting and optimizing flight behavior, making it ideal for navigating through icing. DRL, on the other hand, allows the aircraft to learn and adapt to icing conditions, improving safety and efficiency. The outcome will be a robust flight control system capable of ensuring the safety and reliability of UAM vehicles, even in the challenging weather conditions often found in Canada. This research not only aims to advance the technological capabilities of UAMs but also to make urban aerial transportation a safe, efficient, and viable option in the near future.

Faculty Supervisor:

Reza Faieghi

Student:

Partner:

Gyeongsang National University

Discipline:

Engineering

Sector:

Aerospace

University:

Toronto Metropolitan University

Program: