Real-time adaptive entry trajectory generation with modular policy and deep reinforcement learning-Reference-Cited by-同舟云学术

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Real-time adaptive entry trajectory generation with modular policy and deep reinforcement learning

Published:2023-11 Issue: Volume:142 Page:108594
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Peng Gaoxiang^ORCID,Wang Bo^ORCID,Liu Lei^ORCID,Fan Huijin,Cheng Zhongtao^ORCID

Publisher

Elsevier BV

Subject

Aerospace Engineering

Reference37 articles.

1. Real-time reentry trajectory planning of hypersonic vehicles: a two-step strategy incorporating fuzzy multiobjective transcription and deep neural network;Chai;IEEE Trans. Ind. Electron.,2020

2. Optimal guidance for hypersonic vehicle using analytical solutions and an intelligent reversal strategy;Zhou;Aerosp. Sci. Technol.,2023

3. Dynamic lateral entry guidance logic;Shen;J. Guid. Control Dyn.,2004

4. Constrained predictor-corrector guidance via bank saturation avoidance for low L/D entry vehicles;Liang;Aerosp. Sci. Technol.,2021

5. A novel adaptive lateral reentry guidance algorithm with complex distributed no-fly zones constraints;Hu;Chin. J. Aeronaut.,2022

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3. Predictor-corrector reentry guidance for hypersonic glide vehicles based on high-precision analytical solutions;Aerospace Science and Technology;2024-08

4. Real-time Trajectory Planning for Hypersonic Entry Flight via Curriculum Reinforcement Learning;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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