Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929619842629632 |
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| author | Kim, Taehyeun Kee, Robin Inho Kolmanovsky, Ilya Girard, Anouck |
| author_facet | Kim, Taehyeun Kee, Robin Inho Kolmanovsky, Ilya Girard, Anouck |
| contents | This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification of the commanded reference trajectory ensures that constraints are enforced while the time shift is reduced to zero to effect the rendezvous. Our approach to TSG implementation integrates an LSTM neural network which approximates the time shift parameter as a function of a sequence of past Deputy and Chief spacecraft states. This LSTM neural network is trained offline from simulation data. We report simulation results for RD missions in the Low Earth Orbit (LEO) and on the Molniya orbit to demonstrate the effectiveness of the proposed control scheme. The proposed scheme reduces the time to compute the time shift parameter in most of the scenarios and successfully completes rendezvous missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor Kim, Taehyeun Kee, Robin Inho Kolmanovsky, Ilya Girard, Anouck Systems and Control Artificial Intelligence Machine Learning Robotics This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification of the commanded reference trajectory ensures that constraints are enforced while the time shift is reduced to zero to effect the rendezvous. Our approach to TSG implementation integrates an LSTM neural network which approximates the time shift parameter as a function of a sequence of past Deputy and Chief spacecraft states. This LSTM neural network is trained offline from simulation data. We report simulation results for RD missions in the Low Earth Orbit (LEO) and on the Molniya orbit to demonstrate the effectiveness of the proposed control scheme. The proposed scheme reduces the time to compute the time shift parameter in most of the scenarios and successfully completes rendezvous missions. |
| title | Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor |
| topic | Systems and Control Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2412.05748 |