A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry
We present a new method to design the controller for Mars capsule atmospheric entry using deep neural networks and flight-proven Apollo entry data. The controller is trained to modulate the bank angle with data from the Apollo entry simulations. The neural network controller reproduces the classical...
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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/10.1155/2020/3793740 |
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| author | Hao Wang Tarek A. Elgohary |
| author_facet | Hao Wang Tarek A. Elgohary |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | We present a new method to design the controller for Mars capsule atmospheric entry using deep neural networks and flight-proven Apollo entry data. The controller is trained to modulate the bank angle with data from the Apollo entry simulations. The neural network controller reproduces the classical Apollo results over a variation of entry state initial conditions. Compared to the Apollo controller as a baseline, the present approach achieves the same level of accuracy for both linear and nonlinear entry dynamics. The Apollo-trained controller is then applied to Mars entry missions. As in Earth environment, the controller achieves the desired level of accuracy for Mars missions using both linear and nonlinear entry dynamics with higher uncertainties in the entry states and the atmospheric density. The deep neural network is only trained with data from Apollo reentry simulation in an Earth model and works in both Earth and Mars environments. It achieves the desired landing accuracy for a Mars capsule. This method works with both linear and nonlinear integration and can generate the bank angle commands in real-time without a prestored trajectory. |
| format | Article |
| id | doaj-art-331d5c9e1cb741c4a566f0c08f1f8a5f |
| institution | OA Journals |
| 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-331d5c9e1cb741c4a566f0c08f1f8a5f2025-08-20T02:24:18ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/37937403793740A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric EntryHao Wang0Tarek A. Elgohary1Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USADepartment of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USAWe present a new method to design the controller for Mars capsule atmospheric entry using deep neural networks and flight-proven Apollo entry data. The controller is trained to modulate the bank angle with data from the Apollo entry simulations. The neural network controller reproduces the classical Apollo results over a variation of entry state initial conditions. Compared to the Apollo controller as a baseline, the present approach achieves the same level of accuracy for both linear and nonlinear entry dynamics. The Apollo-trained controller is then applied to Mars entry missions. As in Earth environment, the controller achieves the desired level of accuracy for Mars missions using both linear and nonlinear entry dynamics with higher uncertainties in the entry states and the atmospheric density. The deep neural network is only trained with data from Apollo reentry simulation in an Earth model and works in both Earth and Mars environments. It achieves the desired landing accuracy for a Mars capsule. This method works with both linear and nonlinear integration and can generate the bank angle commands in real-time without a prestored trajectory.http://dx.doi.org/10.1155/2020/3793740 |
| spellingShingle | Hao Wang Tarek A. Elgohary A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry International Journal of Aerospace Engineering |
| title | A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry |
| title_full | A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry |
| title_fullStr | A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry |
| title_full_unstemmed | A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry |
| title_short | A Simple and Accurate Apollo-Trained Neural Network Controller for Mars Atmospheric Entry |
| title_sort | simple and accurate apollo trained neural network controller for mars atmospheric entry |
| url | http://dx.doi.org/10.1155/2020/3793740 |
| work_keys_str_mv | AT haowang asimpleandaccurateapollotrainedneuralnetworkcontrollerformarsatmosphericentry AT tarekaelgohary asimpleandaccurateapollotrainedneuralnetworkcontrollerformarsatmosphericentry AT haowang simpleandaccurateapollotrainedneuralnetworkcontrollerformarsatmosphericentry AT tarekaelgohary simpleandaccurateapollotrainedneuralnetworkcontrollerformarsatmosphericentry |