Multiconstrained Real-Time Entry Guidance Using Deep Neural Networks
In this article, an intelligent predictor-corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning proble...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems Jg. 57; H. 1; S. 325 - 340 |
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| Sprache: | Englisch |
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New York
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | In this article, an intelligent predictor-corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multiconstrained predictor-corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNN-based entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing high-precision, safe, and robust entry guidance for hypersonic vehicles. |
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| AbstractList | In this article, an intelligent predictor-corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multiconstrained predictor-corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNN-based entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing high-precision, safe, and robust entry guidance for hypersonic vehicles. |
| Author | Cheng, Lin Li, Junfeng Jiang, Fanghua Wang, Zhenbo |
| Author_xml | – sequence: 1 givenname: Lin orcidid: 0000-0001-8090-2562 surname: Cheng fullname: Cheng, Lin email: chenglin5580@tsinghua.edu.cn organization: Tsinghua University, Beijing, China – sequence: 2 givenname: Fanghua orcidid: 0000-0001-8411-5603 surname: Jiang fullname: Jiang, Fanghua email: jiangfh@tsinghua.edu.cn organization: Tsinghua University, Beijing, China – sequence: 3 givenname: Zhenbo orcidid: 0000-0002-8979-9765 surname: Wang fullname: Wang, Zhenbo email: zwang124@utk.edu organization: University of Tennessee, Knoxville, TN, USA – sequence: 4 givenname: Junfeng surname: Li fullname: Li, Junfeng email: lijunf@mail.tsinghua.edu.cn organization: Tsinghua University, Beijing, China |
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| SubjectTerms | Aerodynamics Algorithms Approximation algorithms Artificial neural networks Constraint management deep neural networks (DNNs) Earth entry guidance Hypersonic vehicles lateral heading control Neural networks numerical predictor–corrector guidance (NPCG) Prediction algorithms Predictor-corrector methods Real time Real-time systems Terminal constraints Trajectory Trajectory planning Vehicle dynamics |
| Title | Multiconstrained Real-Time Entry Guidance Using Deep Neural Networks |
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