Volume 51 Issue 5
May  2025
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SHI Q,QI R Y,SHE Y C,et al. Online trajectory optimization method for vertical landing phase of reusable launch vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(5):1760-1769 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0299
Citation: SHI Q,QI R Y,SHE Y C,et al. Online trajectory optimization method for vertical landing phase of reusable launch vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(5):1760-1769 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0299

Online trajectory optimization method for vertical landing phase of reusable launch vehicle

doi: 10.13700/j.bh.1001-5965.2023.0299
Funds:

National Natural Science Foundation of China (62373189); National Key Laboratory of Aerospace Structural Mechanics and Control Open Project Funding 

More Information
  • Corresponding author: E-mail:ruiyun.qi@nuaa.edu.cn
  • Received Date: 30 May 2023
  • Accepted Date: 11 Aug 2023
  • Publish Date: 08 Sep 2023
  • In order to overcome the difficulties of ambiguous initial circumstances, intricate process constraints, and strict terminal restrictions in the guidance of reusable launch vehicles, this work suggests an online trajectory optimization technique that combines convex optimization and fourth-order polynomial guidance. The method utilizes a fourth-order polynomial guidance approach considering terminal attitude constraints and fuel optimality as the objective to compute the initial trajectory and terminal landing time. By considering different initial conditions, different trajectories and times are obtained to ensure a successful solution of convex optimization under varying circumstances. Additionally, by applying the initial trajectory to a convex optimization algorithm with the same fuel optimality objective, guidance commands are generated to satisfy process constraints and terminal constraints, enabling a soft landing. Simulation analysis demonstrates that the proposed method exhibits better adaptability and higher reliability to different deviations compared to constant convex optimization algorithms. Furthermore, by doing away with the need to optimize terminal landing time, the suggested approach increases solution efficiency and lowers fuel consumption.

     

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