Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system
Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115057/ http://psasir.upm.edu.my/id/eprint/115057/1/115057.pdf |
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| author | Aslam, Sohaib Chak, Yew-Chung Jaffery, Mujtaba Hussain Varatharajoo, Renuganth Razoumny, Yury |
| author_facet | Aslam, Sohaib Chak, Yew-Chung Jaffery, Mujtaba Hussain Varatharajoo, Renuganth Razoumny, Yury |
| author_sort | Aslam, Sohaib |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example. © 2024 COSPAR |
| first_indexed | 2025-11-15T14:24:19Z |
| format | Article |
| id | upm-115057 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:24:19Z |
| publishDate | 2024 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1150572025-02-18T07:26:46Z http://psasir.upm.edu.my/id/eprint/115057/ Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system Aslam, Sohaib Chak, Yew-Chung Jaffery, Mujtaba Hussain Varatharajoo, Renuganth Razoumny, Yury Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example. © 2024 COSPAR Elsevier Ltd 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/115057/1/115057.pdf Aslam, Sohaib and Chak, Yew-Chung and Jaffery, Mujtaba Hussain and Varatharajoo, Renuganth and Razoumny, Yury (2024) Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system. Advances in Space Research, 74 (7). pp. 3234-3255. ISSN 0273-1177; eISSN: 1879-1948 https://linkinghub.elsevier.com/retrieve/pii/S0273117724007300 10.1016/j.asr.2024.07.034 |
| spellingShingle | Aslam, Sohaib Chak, Yew-Chung Jaffery, Mujtaba Hussain Varatharajoo, Renuganth Razoumny, Yury Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title | Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title_full | Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title_fullStr | Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title_full_unstemmed | Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title_short | Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system |
| title_sort | deep learning based fuzzy-mpc controller for satellite combined energy and attitude control system |
| url | http://psasir.upm.edu.my/id/eprint/115057/ http://psasir.upm.edu.my/id/eprint/115057/ http://psasir.upm.edu.my/id/eprint/115057/ http://psasir.upm.edu.my/id/eprint/115057/1/115057.pdf |