  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).
