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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.25309 |
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| _version_ | 1866916071143899136 |
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| author | Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal |
| author_facet | Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal |
| contents | This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-Enabled Predictive Control (DeePC). The approach eliminates explicit hydrodynamic modeling by exploiting measured input-output trajectories to predict and optimize future system behavior. Classic DeePC is employed for heading control, while a cascaded DeePC architecture with loop-frequency separation is proposed for depth regulation, extending DeePC to plants whose dominant output evolves significantly slower than the actuator bandwidth. For 3-D waypoint path following, the Adaptive Line-of-Sight (ALOS) guidance law is extended to a predictive multistep formulation (PALOS) that supplies the horizon-consistent reference required by receding-horizon predictive controllers. All methods are validated in high-fidelity 6 degrees of freedom simulation on the REMUS~100 AUV under nominal operation, ocean-current disturbances, operation beyond the data regime, and 3-D waypoint path following, consistently outperforming the corresponding state-of-the-art benchmarks. In 3-D waypoint path following, the framework reduces cross-track error by approximately 28\% relative to the ALOS-PI/PID baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal Systems and Control This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-Enabled Predictive Control (DeePC). The approach eliminates explicit hydrodynamic modeling by exploiting measured input-output trajectories to predict and optimize future system behavior. Classic DeePC is employed for heading control, while a cascaded DeePC architecture with loop-frequency separation is proposed for depth regulation, extending DeePC to plants whose dominant output evolves significantly slower than the actuator bandwidth. For 3-D waypoint path following, the Adaptive Line-of-Sight (ALOS) guidance law is extended to a predictive multistep formulation (PALOS) that supplies the horizon-consistent reference required by receding-horizon predictive controllers. All methods are validated in high-fidelity 6 degrees of freedom simulation on the REMUS~100 AUV under nominal operation, ocean-current disturbances, operation beyond the data regime, and 3-D waypoint path following, consistently outperforming the corresponding state-of-the-art benchmarks. In 3-D waypoint path following, the framework reduces cross-track error by approximately 28\% relative to the ALOS-PI/PID baseline. |
| title | Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.25309 |