@INPROCEEDINGS{Mana2409:Koopman,
AUTHOR="Zeyad Mahmoud Manaa and Ayman Abdallah and Mohammad A. Abido and Syed Saad
Azhar Ali",
TITLE="{Koopman-LQR} Controller for Quadrotor {UAVs} From Data",
BOOKTITLE="2024 IEEE International Conference on Smart Mobility (SM) (SM'2024)",
ADDRESS="Niagara Falls, Canada",
PAGES=20,
DAYS=16,
MONTH=sep,
YEAR=2024,
ABSTRACT="Quadrotor systems are common and beneficial for many fields, but their
intricate behavior often makes it challenging to design effective and
optimal control strategies. Some traditional approaches to nonlinear
control often rely on local linearizations or complex nonlinear models,
which can be inaccurate or computationally expensive. We present a
data-driven approach to identify the dynamics of a given quadrotor system
using Koopman operator theory. Koopman theory offers a framework for
representing nonlinear dynamics as linear operators acting on observable
functions of the state space. This allows to approximate nonlinear systems
with globally linear models in a higher dimensional space, which can be
analyzed and controlled using standard linear optimal control techniques.
We leverage the method of extended dynamic mode decomposition (EDMD) to
identify Koopman operator from data with total least squares. We
demonstrate that the identified model can be stabilized and controllable by
designing a controller using linear quadratic regulator (LQR)."
}