An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs
In addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/12/1008 |
| _version_ | 1797382439707869184 |
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| author | Linfei Hou Honglin Liu Ting Yang Shuaibin An Rui Wang |
| author_facet | Linfei Hou Honglin Liu Ting Yang Shuaibin An Rui Wang |
| author_sort | Linfei Hou |
| collection | DOAJ |
| description | In addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes an intelligent morphing decision method based on deep neural networks (DNNs) for the autonomous morphing decision problem of hypersonic boost-glide morphing vehicles under process constraints. Firstly, we established a dynamic model of a hypersonic boost-glide morphing vehicle with a continuously variable sweep angle. Then, in order to address the decision optimality problem considering errors and the heat flux density constraint problem during the gliding process, interference was introduced to the datum trajectory in segments. Subsequently, re-optimization was performed to generate a trajectory sample library, which was used to train an intelligent decision-maker using a DNN. The simulation results demonstrated that, compared with the conventional programmatic morphing approach, the intelligent morphing decision maker could dynamically determine the sweep angle based on the current flight state, leading to improved range while still adhering to the heat flux density constraint. This validates the effectiveness and robustness of the proposed intelligent decision-maker. |
| first_indexed | 2024-03-08T21:05:26Z |
| format | Article |
| id | doaj.art-5f5284ddcccd460d809e45f28ec62fc5 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-08T21:05:26Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-5f5284ddcccd460d809e45f28ec62fc52023-12-22T13:45:12ZengMDPI AGAerospace2226-43102023-11-011012100810.3390/aerospace10121008An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNsLinfei Hou0Honglin Liu1Ting Yang2Shuaibin An3Rui Wang4School of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, ChinaSchool of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, ChinaBeijing Aerospace Technology Institute, Beijing 100074, ChinaSchool of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, ChinaSchool of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, ChinaIn addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes an intelligent morphing decision method based on deep neural networks (DNNs) for the autonomous morphing decision problem of hypersonic boost-glide morphing vehicles under process constraints. Firstly, we established a dynamic model of a hypersonic boost-glide morphing vehicle with a continuously variable sweep angle. Then, in order to address the decision optimality problem considering errors and the heat flux density constraint problem during the gliding process, interference was introduced to the datum trajectory in segments. Subsequently, re-optimization was performed to generate a trajectory sample library, which was used to train an intelligent decision-maker using a DNN. The simulation results demonstrated that, compared with the conventional programmatic morphing approach, the intelligent morphing decision maker could dynamically determine the sweep angle based on the current flight state, leading to improved range while still adhering to the heat flux density constraint. This validates the effectiveness and robustness of the proposed intelligent decision-maker.https://www.mdpi.com/2226-4310/10/12/1008morphing flight vehicleintelligent decision-makinghypersonic boost-glide vehicle |
| spellingShingle | Linfei Hou Honglin Liu Ting Yang Shuaibin An Rui Wang An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs Aerospace morphing flight vehicle intelligent decision-making hypersonic boost-glide vehicle |
| title | An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs |
| title_full | An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs |
| title_fullStr | An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs |
| title_full_unstemmed | An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs |
| title_short | An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs |
| title_sort | intelligent autonomous morphing decision approach for hypersonic boost glide vehicles based on dnns |
| topic | morphing flight vehicle intelligent decision-making hypersonic boost-glide vehicle |
| url | https://www.mdpi.com/2226-4310/10/12/1008 |
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