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Research on Aircraft Standard Trajectory Tracking Guidance and Formation Keeping based on Reinforcement Learning
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Qinghua TENG1, Junpeng HUI2, Tianren LI1, Ben YANG1
Missiles and Space Vehicles | 2025, 48(2) : 60 - 68
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Missiles and Space Vehicles | 2025, 48(2): 60-68
• Guidance, Navigation and Control •
Research on Aircraft Standard Trajectory Tracking Guidance and Formation Keeping based on Reinforcement Learning
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Qinghua TENG1, Junpeng HUI2, Tianren LI1, Ben YANG1
Affiliations
  • 1Research & Development Center,China Academy of Launch Vehicle Technology,Beijing,100076
  • 2Beijing Institute of Space Long March Vehicle,Beijing,100076
Published: 2025-04-25 doi: 10.7654/j.issn.2097-1974.20250208
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The intelligent upgrade of the aircraft has put forward new requirements for guidance capabilities, and traditional algorithms perform poorly in tracking spatial three-dimensional trajectories under biased conditions. An aircraft trajectory tracking guidance method is designed based on the TD3 reinforcement learning algorithm. Through the action space in the form of deviation, the penalty term in the reward function and the guidance of the rate of change of distance, problems such as difficult convergence of algorithm training, large fluctuations in control quantity, and large cumulative deviation at the middle and final shift points are solved. Compared with the traditional LQR algorithm, the reinforcement learning guidance algorithm has significantly improved guidance accuracy and deviation adaptability, and has good versatility, which can be applied to small-scale formation maintenance issues.

TD3 Algorithm  /  standard trajectory guidance  /  reinforcement learning  /  formation keeping  /  Monte Calo simulation
Qinghua TENG, Junpeng HUI, Tianren LI, Ben YANG. Research on Aircraft Standard Trajectory Tracking Guidance and Formation Keeping based on Reinforcement Learning[J]. Missiles and Space Vehicles, 2025 , 48 (2) : 60 -68 . DOI: 10.7654/j.issn.2097-1974.20250208
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Article Info
doi: 10.7654/j.issn.2097-1974.20250208
  • Receive Date：2024-05-21
  • Online Date：2025-07-21
  • Published：2025-04-25
Article Data
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  • Received：2024-05-21
  • Revised：2024-05-31
Affiliations
    1Research & Development Center,China Academy of Launch Vehicle Technology,Beijing,100076
    2Beijing Institute of Space Long March Vehicle,Beijing,100076
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