Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909743312797696 |
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| author | El-Hussieny, Haitham |
| author_facet | El-Hussieny, Haitham |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control El-Hussieny, Haitham Robotics 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. |
| title | Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.13795 |