Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911659419762688 |
|---|---|
| author | Takahashi, Yuta Sakai, Shin-ichiro |
| author_facet | Takahashi, Yuta Sakai, Shin-ichiro |
| contents | This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude control. This study derives a unique, continuous, and power-optimal current solution via sequential convex programming and approximates it using a multilayer perceptron model. The effectiveness of our strategy was demonstrated through numerical simulations and experimental trials on the formation and attitude control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00548 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control Takahashi, Yuta Sakai, Shin-ichiro Multiagent Systems This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude control. This study derives a unique, continuous, and power-optimal current solution via sequential convex programming and approximates it using a multilayer perceptron model. The effectiveness of our strategy was demonstrated through numerical simulations and experimental trials on the formation and attitude control. |
| title | Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2412.00548 |