This paper presents a data-driven control framework for quadrotor systems that integrates a deep Koopman operator with model predictive control (DK-MPC). The deep Koopman operator is trained on sampled flight data to construct a high-dimensional latent representation in which the nonlinear quadrotor dynamics are approximated by linear models. This linearization enables the application of MPC to efficiently optimize control actions over a finite prediction horizon, ensuring accurate trajectory tracking and stabilization. The proposed DK-MPC approach is validated through a series of trajectory-following and point-stabilization numerical experiments, where it demonstrates superior tracking accuracy and significantly lower computation time compared to conventional nonlinear MPC. These results highlight the potential of Koopman-based learning methods to handle complex quadrotor dynamics while meeting the real-time requirements of embedded flight control. Future work will focus on extending the framework to more agile flight scenarios and improving robustness against external disturbances.
Haitham El-Hussieny (Tue,) studied this question.