RL-based Control of UAS Subject to Significant Disturbance
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914410491019264 |
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| author | Chakraborty, Kousheek Hof, Thijs Alharbat, Ayham Mersha, Abeje |
| author_facet | Chakraborty, Kousheek Hof, Thijs Alharbat, Ayham Mersha, Abeje |
| contents | This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RL-based Control of UAS Subject to Significant Disturbance Chakraborty, Kousheek Hof, Thijs Alharbat, Ayham Mersha, Abeje Robotics Machine Learning Systems and Control This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance. |
| title | RL-based Control of UAS Subject to Significant Disturbance |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2504.08114 |