Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.12920 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911973166284800 |
|---|---|
| author | Wang, Kun Chen, Zheng Lu, Fangmin Li, Jun |
| author_facet | Wang, Kun Chen, Zheng Lu, Fangmin Li, Jun |
| contents | This paper addresses an optimal guidance problem concerning the vertical landing of a lunar lander with the objective of minimizing fuel consumption. The vertical landing imposes a final attitude constraint, which is treated as a final control constraint. To handle this constraint, we propose a nonnegative small regularization term to augment the original cost functional. This ensures the satisfaction of the final control constraint in accordance with Pontryagin's Minimum Principle. By leveraging the necessary conditions for optimality, we establish a parameterized system that facilitates the generation of numerous optimal trajectories, which contain the nonlinear mapping from the flight state to the optimal guidance command. Subsequently, a neural network is trained to approximate such mapping. Finally, numerical examples are presented to validate the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural-Network-Based Optimal Guidance for Lunar Vertical Landing Wang, Kun Chen, Zheng Lu, Fangmin Li, Jun Optimization and Control This paper addresses an optimal guidance problem concerning the vertical landing of a lunar lander with the objective of minimizing fuel consumption. The vertical landing imposes a final attitude constraint, which is treated as a final control constraint. To handle this constraint, we propose a nonnegative small regularization term to augment the original cost functional. This ensures the satisfaction of the final control constraint in accordance with Pontryagin's Minimum Principle. By leveraging the necessary conditions for optimality, we establish a parameterized system that facilitates the generation of numerous optimal trajectories, which contain the nonlinear mapping from the flight state to the optimal guidance command. Subsequently, a neural network is trained to approximate such mapping. Finally, numerical examples are presented to validate the proposed method. |
| title | Neural-Network-Based Optimal Guidance for Lunar Vertical Landing |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2402.12920 |