Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks
A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8165147 |
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| _version_ | 1832549944259510272 |
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| author | Hongjue Li Yunfeng Dong Peiyun Li |
| author_facet | Hongjue Li Yunfeng Dong Peiyun Li |
| author_sort | Hongjue Li |
| collection | DOAJ |
| description | A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation. In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples. These training samples are generated by solving multiple optimal control problems with successive iterations. Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data. Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network. |
| format | Article |
| id | doaj-art-7c38d2abf14e4365b6c8c649bd9cbbff |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-7c38d2abf14e4365b6c8c649bd9cbbff2025-02-03T06:08:07ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/81651478165147Real-Time Optimal Approach and Capture of ENVISAT Based on Neural NetworksHongjue Li0Yunfeng Dong1Peiyun Li2School of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaA neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation. In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples. These training samples are generated by solving multiple optimal control problems with successive iterations. Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data. Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network.http://dx.doi.org/10.1155/2020/8165147 |
| spellingShingle | Hongjue Li Yunfeng Dong Peiyun Li Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks International Journal of Aerospace Engineering |
| title | Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks |
| title_full | Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks |
| title_fullStr | Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks |
| title_full_unstemmed | Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks |
| title_short | Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks |
| title_sort | real time optimal approach and capture of envisat based on neural networks |
| url | http://dx.doi.org/10.1155/2020/8165147 |
| work_keys_str_mv | AT hongjueli realtimeoptimalapproachandcaptureofenvisatbasedonneuralnetworks AT yunfengdong realtimeoptimalapproachandcaptureofenvisatbasedonneuralnetworks AT peiyunli realtimeoptimalapproachandcaptureofenvisatbasedonneuralnetworks |