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<p>This paper presents an improved two-phase sequential convex programming (SCP) algorithm for reentry trajectory optimization. The continuous-time optimal control problem is first discretized using the Chebyshev pseudospectral method (CPM). A conformal mapping is introduced to redistribute the Chebyshev–Gauss–Lobatto (CGL) nodes, which effectively mitigates the ill-conditioning of the differentiation matrix and enhances numerical stability. The influence of the trust-region radius on the descent behavior of the penalty function is then analyzed, leading to a two-phase optimization strategy. In phase I, line-search is adopted to rapidly obtain a feasible solution, thereby avoiding convergence delays caused by prematurely large penalty coefficients. In phase II, the step size is finely tuned using trust-region constraints, ensuring both convergence accuracy and improved robustness. Numerical simulations demonstrate that the proposed algorithm achieves superior performance in terms of convergence speed and solution accuracy, while effectively eliminating the numerical oscillations typically induced by excessive penalty coefficients in conventional penalty methods. The framework provides a reliable and efficient numerical approach for reentry trajectory optimization.</p>

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Improved two-phase sequential convex programming for reentry trajectory optimization

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Guangbin Cai,
  • Hao Wei,
  • Hui Xu,
  • Bin Zhou,
  • Mingzhe Hou

摘要

This paper presents an improved two-phase sequential convex programming (SCP) algorithm for reentry trajectory optimization. The continuous-time optimal control problem is first discretized using the Chebyshev pseudospectral method (CPM). A conformal mapping is introduced to redistribute the Chebyshev–Gauss–Lobatto (CGL) nodes, which effectively mitigates the ill-conditioning of the differentiation matrix and enhances numerical stability. The influence of the trust-region radius on the descent behavior of the penalty function is then analyzed, leading to a two-phase optimization strategy. In phase I, line-search is adopted to rapidly obtain a feasible solution, thereby avoiding convergence delays caused by prematurely large penalty coefficients. In phase II, the step size is finely tuned using trust-region constraints, ensuring both convergence accuracy and improved robustness. Numerical simulations demonstrate that the proposed algorithm achieves superior performance in terms of convergence speed and solution accuracy, while effectively eliminating the numerical oscillations typically induced by excessive penalty coefficients in conventional penalty methods. The framework provides a reliable and efficient numerical approach for reentry trajectory optimization.