Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908645205213184 |
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| author | Kumar, Suraj Rallapalli, Aditya GVP, Bharat Kumar |
| author_facet | Kumar, Suraj Rallapalli, Aditya GVP, Bharat Kumar |
| contents | Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission Kumar, Suraj Rallapalli, Aditya GVP, Bharat Kumar Systems and Control Machine Learning Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations. |
| title | Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2511.08612 |