利用深度强化学习实现不同质量的自适应卫星姿态控制

Wiebke Retagne, Jonas Dauer, Günther Waxenegger-Wilfing
{"title":"利用深度强化学习实现不同质量的自适应卫星姿态控制","authors":"Wiebke Retagne, Jonas Dauer, Günther Waxenegger-Wilfing","doi":"10.3389/frobt.2024.1402846","DOIUrl":null,"url":null,"abstract":"Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional–integral–derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"89 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive satellite attitude control for varying masses using deep reinforcement learning\",\"authors\":\"Wiebke Retagne, Jonas Dauer, Günther Waxenegger-Wilfing\",\"doi\":\"10.3389/frobt.2024.1402846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional–integral–derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.\",\"PeriodicalId\":504612,\"journal\":{\"name\":\"Frontiers in Robotics and AI\",\"volume\":\"89 10\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Robotics and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frobt.2024.1402846\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Robotics and AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frobt.2024.1402846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的航天器姿态控制通常在很大程度上依赖于航天器的尺寸和质量信息。在主动清除碎片的情况下,这些特征无法事先知道,因为碎片可以是任何形状或质量。此外,也无法测量轨道上卫星和碎片物体组合系统的质量。因此,开发一种能从其他测量中提取卫星系统质量信息的自适应卫星姿态控制至关重要。作者建议使用深度强化学习(DRL)算法,利用堆叠观测数据来处理千差万别的质量。在 Basilisk 软件中对卫星进行了模拟,并使用蒙特卡罗模拟对控制性能进行了评估。结果表明,与用于航天器姿态控制的经典比例-积分-派生(PID)控制器相比,使用堆叠观测数据的 DRL 更具优势。该算法能够适应,尤其是在物理特性不断变化的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive satellite attitude control for varying masses using deep reinforcement learning
Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional–integral–derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信