Abstract
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.
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Acknowledgment
This work is funded by the Aeronautical Science Foundation of China under Grant no. 2018ZC51031.
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Wu, T., Wang, H., Liu, Y., Yu, Y., Lun, Y. (2022). Learning-Based Predictive-Corrector Reentry Guidance for Hypersonic Vehicles. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_248
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DOI: https://doi.org/10.1007/978-981-16-9492-9_248
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