Aerospace & Defense Technology - December 2024 - 26

Tech Briefs
AI-Trained Vehicles Can Adjust to Extreme Turbulence
on the Fly
Researchers at Caltech took an important step toward using reinforcement learning 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.
California Institute of Technology, Pasadena, CA
I
n 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
published 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. " Extreme turbulence
also arises at the interface between two
different shear flows - for example,
when high-speed winds meet stagnation
around a tall building. Therefore, UAVs
in urban settings need to be able to compensate
for such sudden changes. FAL26
The
team's experimental setup in the John W. Lucas Wind Tunnel at Caltech. A large black cylinder with
a plate attached at the front creates irregular turbulence under the tunnel's high-speed winds. The airfoil
wing system with eight pressure sensors and a pitot tube is mounted on a load cell to measure the lift
experienced by the system. (Image: Caltech)
CON gives these vehicles a way to understand
the turbulence that is coming and
make necessary adjustments. "
FALCON is not the first UAV control
strategy to use reinforcement learning.
However, previous strategies have not tried
to learn the underlying model that truly
represents how turbulent winds work.
Instead, they have all been model-free
methods. Such methods focus on maximizing
a reward function that cannot be
used to tackle different settings, such as
different wind conditions or vehicle configurations,
without retraining because
they focus on just one environment.
" That's not so good in the physical
world, where we know that situations
can change drastically and quickly, " says
Anima Anandkumar, the Bren Professor
of Computing and Mathematical Sciences
at Caltech and an author of the new
paper. " We need the AI to learn the
underlying model of turbulence well so
mobilityengineeringtech.com
that it can take action based on how it
thinks the wind will change. "
" Advancements in fundamental AI
will change the face of the aviation
industry, enhancing safety, efficiency,
and performance across a range of platforms,
including passenger planes, UAVs,
and carrier aircraft. These innovations
promise to make air travel and operations
smarter, safer, and more streamlined, "
says Kamyar Azizzadenesheli, a
co-author from NVIDIA.
As the FALCON acronym says, the
strategy is based on Fourier methods,
meaning that it relies on the use of sinusoids,
or periodic waves, to represent signals
- here, wind conditions. The waves
provide a good approximation of standard
wind motions, keeping needed
computation to a minimum. Within
those waves, when extreme turbulence
arises, the unsteadiness shows up as a
noticeable change in frequency.
Aerospace & Defense Technology, December 2024

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Aerospace & Defense Technology - December 2024

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