{"title":"基于学习的控制,用于部署和回收旋转系留卫星编队系统","authors":"","doi":"10.1016/j.actaastro.2024.09.061","DOIUrl":null,"url":null,"abstract":"<div><div>This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. Based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy.</div></div>","PeriodicalId":44971,"journal":{"name":"Acta Astronautica","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning-based control for deployment and retrieval of a spinning tethered satellite formation system\",\"authors\":\"\",\"doi\":\"10.1016/j.actaastro.2024.09.061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. Based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy.</div></div>\",\"PeriodicalId\":44971,\"journal\":{\"name\":\"Acta Astronautica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Astronautica\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0094576524005630\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Astronautica","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0094576524005630","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Learning-based control for deployment and retrieval of a spinning tethered satellite formation system
This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. Based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy.
期刊介绍:
Acta Astronautica is sponsored by the International Academy of Astronautics. Content is based on original contributions in all fields of basic, engineering, life and social space sciences and of space technology related to:
The peaceful scientific exploration of space,
Its exploitation for human welfare and progress,
Conception, design, development and operation of space-borne and Earth-based systems,
In addition to regular issues, the journal publishes selected proceedings of the annual International Astronautical Congress (IAC), transactions of the IAA and special issues on topics of current interest, such as microgravity, space station technology, geostationary orbits, and space economics. Other subject areas include satellite technology, space transportation and communications, space energy, power and propulsion, astrodynamics, extraterrestrial intelligence and Earth observations.