Optimizing AUV speed dynamics with a data-driven Koopman operator approach
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915195293532160 |
|---|---|
| author | Liu, Zhiliang Zhao, Xin Cai, Peng Cong, Bing |
| author_facet | Liu, Zhiliang Zhao, Xin Cai, Peng Cong, Bing |
| contents | Autonomous Underwater Vehicles (AUVs) play an essential role in modern ocean exploration, and their speed control systems are fundamental
to their efficient operation. Like many other robotic systems, AUVs exhibit multivariable nonlinear dynamics and face various constraints,
including state limitations, input constraints, and constraints on the increment input, making controller design challenging
and requiring significant effort and time. This paper addresses these challenges by employing a data-driven Koopman operator theory combined
with Model Predictive Control (MPC), which takes into account the aforementioned constraints. The proposed approach not only ensures
the performance of the AUV under state and input limitations but also considers the variation in incremental input to prevent
rapid and potentially damaging changes to the vehicle's operation. Additionally, we develop a platform based on ROS2 and Gazebo
to validate the effectiveness of the proposed algorithms, providing new control strategies for underwater vehicles against the complex and dynamic nature of underwater environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing AUV speed dynamics with a data-driven Koopman operator approach Liu, Zhiliang Zhao, Xin Cai, Peng Cong, Bing Systems and Control Robotics Dynamical Systems Autonomous Underwater Vehicles (AUVs) play an essential role in modern ocean exploration, and their speed control systems are fundamental to their efficient operation. Like many other robotic systems, AUVs exhibit multivariable nonlinear dynamics and face various constraints, including state limitations, input constraints, and constraints on the increment input, making controller design challenging and requiring significant effort and time. This paper addresses these challenges by employing a data-driven Koopman operator theory combined with Model Predictive Control (MPC), which takes into account the aforementioned constraints. The proposed approach not only ensures the performance of the AUV under state and input limitations but also considers the variation in incremental input to prevent rapid and potentially damaging changes to the vehicle's operation. Additionally, we develop a platform based on ROS2 and Gazebo to validate the effectiveness of the proposed algorithms, providing new control strategies for underwater vehicles against the complex and dynamic nature of underwater environments. |
| title | Optimizing AUV speed dynamics with a data-driven Koopman operator approach |
| topic | Systems and Control Robotics Dynamical Systems |
| url | https://arxiv.org/abs/2503.09628 |