A learning-based predictive-corrector guidance method for hypersonic vehicles with a high lift-to-drag ratio is proposed in this paper. First, based on the quasi equilibrium-glide condition, a traditional predictive-corrector guidance algorithm is employed to address reentry guidance with path constraint. Then, to avoid the ubiquitous phenomenon of large phugoid oscillation, a learning-based altitude rate feedback mechanism is proposed. The feedback gain is scheduled adaptively by a deep reinforcement learning strategy to enhance the adaptability and robustness of the guidance algorithm in different flight environments. Finally, the numerical simulation demonstrates the effectiveness of the proposed algorithm.
Learning-Based Predictive-Corrector Reentry Guidance for Hypersonic Vehicles
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Chapter : 248 ; 2518-2527
2022-03-18
10 pages
Article/Chapter (Book)
Electronic Resource
English
Learning-Based Predictive-Corrector Reentry Guidance for Hypersonic Vehicles
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