Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter
This study focuses on the transfer alignment issue between a master spacecraft and a slave spacecraft for the scenario in which the slave spacecraft is mounted on the master satellite before release and should be ready to depart and perform its space mission independently. The challenge of the trans...
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2774 |
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| author | Kai Xiong Peng Zhou Xiangyu Huang |
| author_facet | Kai Xiong Peng Zhou Xiangyu Huang |
| author_sort | Kai Xiong |
| collection | DOAJ |
| description | This study focuses on the transfer alignment issue between a master spacecraft and a slave spacecraft for the scenario in which the slave spacecraft is mounted on the master satellite before release and should be ready to depart and perform its space mission independently. The challenge of the transfer alignment is to estimate the attitude and calibration parameters of the gyroscope unit (GU) on the slave spacecraft based on the attitude determination system (ADS) of the master spacecraft. To improve the accuracy and rapidity of the transfer alignment, a novel attitude plus angular rate matching scheme is presented using fused sensor information on the master spacecraft. Accordingly, a fifteen-dimensional state-space model is derived to estimate the spacecraft attitude, the GU bias, scale factor error and misalignment simultaneously. A Q-learning Kalman filter (QKF) is designed to fine tune the process noise covariance matrix related to the calibration parameters, which benefits the state estimation performance. The simulation results show that the presented attitude plus angular rate matching scheme performs better than the traditional attitude matching scheme, and the QKF outperforms the standard Kalman filter (KF) and the adaptive Kalman filter (AKF). |
| format | Article |
| id | doaj-art-e7adffb86f944542a68f951edf3a1868 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e7adffb86f944542a68f951edf3a18682025-08-20T02:31:20ZengMDPI AGSensors1424-82202025-04-01259277410.3390/s25092774Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman FilterKai Xiong0Peng Zhou1Xiangyu Huang2Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100190, ChinaScience and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100190, ChinaScience and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100190, ChinaThis study focuses on the transfer alignment issue between a master spacecraft and a slave spacecraft for the scenario in which the slave spacecraft is mounted on the master satellite before release and should be ready to depart and perform its space mission independently. The challenge of the transfer alignment is to estimate the attitude and calibration parameters of the gyroscope unit (GU) on the slave spacecraft based on the attitude determination system (ADS) of the master spacecraft. To improve the accuracy and rapidity of the transfer alignment, a novel attitude plus angular rate matching scheme is presented using fused sensor information on the master spacecraft. Accordingly, a fifteen-dimensional state-space model is derived to estimate the spacecraft attitude, the GU bias, scale factor error and misalignment simultaneously. A Q-learning Kalman filter (QKF) is designed to fine tune the process noise covariance matrix related to the calibration parameters, which benefits the state estimation performance. The simulation results show that the presented attitude plus angular rate matching scheme performs better than the traditional attitude matching scheme, and the QKF outperforms the standard Kalman filter (KF) and the adaptive Kalman filter (AKF).https://www.mdpi.com/1424-8220/25/9/2774spacecraftattitude determinationtransfer alignmentQ-learningKalman filter |
| spellingShingle | Kai Xiong Peng Zhou Xiangyu Huang Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter Sensors spacecraft attitude determination transfer alignment Q-learning Kalman filter |
| title | Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter |
| title_full | Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter |
| title_fullStr | Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter |
| title_full_unstemmed | Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter |
| title_short | Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter |
| title_sort | inter spacecraft rapid transfer alignment based on attitude plus angular rate matching using q learning kalman filter |
| topic | spacecraft attitude determination transfer alignment Q-learning Kalman filter |
| url | https://www.mdpi.com/1424-8220/25/9/2774 |
| work_keys_str_mv | AT kaixiong interspacecraftrapidtransferalignmentbasedonattitudeplusangularratematchingusingqlearningkalmanfilter AT pengzhou interspacecraftrapidtransferalignmentbasedonattitudeplusangularratematchingusingqlearningkalmanfilter AT xiangyuhuang interspacecraftrapidtransferalignmentbasedonattitudeplusangularratematchingusingqlearningkalmanfilter |