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With growing demand for fuel-efficient space missions, aeroassisted orbital transfer (AOT) has become a critical focus in orbital dynamics research. This paper investigates fuel-optimal AOT trajectory optimization under complex constraints using sequential convex programming (SCP). The proposed method introduces auxiliary control variables to decouple state and control dynamics, effectively mitigating high-frequency oscillations caused by linearization. Successive linearization is applied to highly nonlinear dynamics, boundary equality constraints, path inequality constraints, and nonlinear performance indices. Numerical simulations demonstrate that the SCP algorithm outperforms GPOPS in solving fuel-optimal AOT problems, achieving superior solution optimality, path constraint satisfaction, and computational efficiency.

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Aeroassisted Orbital Transfer Optimization Using Sequential Convex Programming

  • 会议论文

  • 发表日期：

    2026年

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  • Yu Qiao,
  • Ping Ma,
  • Ming Yang,
  • Tao Chao

摘要

With growing demand for fuel-efficient space missions, aeroassisted orbital transfer (AOT) has become a critical focus in orbital dynamics research. This paper investigates fuel-optimal AOT trajectory optimization under complex constraints using sequential convex programming (SCP). The proposed method introduces auxiliary control variables to decouple state and control dynamics, effectively mitigating high-frequency oscillations caused by linearization. Successive linearization is applied to highly nonlinear dynamics, boundary equality constraints, path inequality constraints, and nonlinear performance indices. Numerical simulations demonstrate that the SCP algorithm outperforms GPOPS in solving fuel-optimal AOT problems, achieving superior solution optimality, path constraint satisfaction, and computational efficiency.