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In nature, flying animals sense coming changes in their surroundings, including the onset of sudden turbulence, and quickly adjust to stay safe. Engineers who design aircraft would like to give their vehicles the same ability to predict incoming disturbances and respond appropriately.

Indeed, disasters such as the fatal Singapore Airlines flight this past May in which more than 100 passengers were injured after the plane encountered severe turbulence, could be avoided if aircraft had such automatic sensing and prediction capabilities combined with mechanisms to stabilize the vehicle.

Now a team of researchers from Caltech's Center for Autonomous Systems and Technologies (CAST) and Nvidia has taken an important step toward such capabilities. In a new paper in the journal npj Robotics, the team describes a control strategy they have developed for unmanned aerial vehicles, or UAVs, called FALCON (Fourier Adaptive Learning and CONtrol).

The strategy uses reinforcement learning, a form of artificial intelligence, to adaptively learn how turbulent wind can change over time and then uses that knowledge to control a UAV based on what it is experiencing in real time.

"Spontaneous turbulence has major consequences for everything from civilian flights to drones. With climate change, extreme weather events that cause this type of turbulence are on the rise," says Mory Gharib, the Hans W. Liepmann Professor of Aeronautics and Medical Engineering, the Booth-Kresa Leadership Chair of CAST, and an author of the new paper.

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