Aggressiveness-Aware Learning-based Control of Quadrotor UAVs with Safety Guarantees
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908852288487424 |
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| author | Colombo, Leonardo Beckers, Thomas Giribet, Juan |
| author_facet | Colombo, Leonardo Beckers, Thomas Giribet, Juan |
| contents | This paper presents an aggressiveness-aware control framework for quadrotor UAVs that integrates learning-based oracles to mitigate the effects of unknown disturbances. Starting from a nominal tracking controller on $\mathrm{SE}(3)$, unmodeled generalized forces and moments are estimated using a learning-based oracle and compensated in the control inputs. An aggressiveness-aware gain scheduling mechanism adapts the feedback gains based on probabilistic model-error bounds, enabling reduced feedback-induced aggressiveness while guaranteeing a prescribed practical exponential tracking performance. The proposed approach makes explicit the trade-off between model accuracy, robustness, and control aggressiveness, and provides a principled way to exploit learning for safer and less aggressive quadrotor maneuvers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21936 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Aggressiveness-Aware Learning-based Control of Quadrotor UAVs with Safety Guarantees Colombo, Leonardo Beckers, Thomas Giribet, Juan Systems and Control Optimization and Control This paper presents an aggressiveness-aware control framework for quadrotor UAVs that integrates learning-based oracles to mitigate the effects of unknown disturbances. Starting from a nominal tracking controller on $\mathrm{SE}(3)$, unmodeled generalized forces and moments are estimated using a learning-based oracle and compensated in the control inputs. An aggressiveness-aware gain scheduling mechanism adapts the feedback gains based on probabilistic model-error bounds, enabling reduced feedback-induced aggressiveness while guaranteeing a prescribed practical exponential tracking performance. The proposed approach makes explicit the trade-off between model accuracy, robustness, and control aggressiveness, and provides a principled way to exploit learning for safer and less aggressive quadrotor maneuvers. |
| title | Aggressiveness-Aware Learning-based Control of Quadrotor UAVs with Safety Guarantees |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2602.21936 |