{"ID":2880671,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13795","arxiv_id":"2508.13795","title":"Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control","abstract":"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.","short_abstract":"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 approx...","url_abs":"https://arxiv.org/abs/2508.13795","url_pdf":"https://arxiv.org/pdf/2508.13795v1","authors":"[\"Haitham El-Hussieny\"]","published":"2025-08-19T12:54:56Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}