针对全要素终端状态约束下的亚轨道可重复使用飞行器再入滑翔轨迹规划精度不足、难收敛问题，提出了一种结合序列凸优化与自适应网格更新约束同伦模型预测凸规划的高精度求解算法，实现弱初值依赖条件下再入轨迹规划过程的高精度可靠收敛。首先，对部分终端约束进行松弛处理，并在凸优化求解过程中将信赖域作为软约束，定义反映线性化误差及约束违反程度的增广指标函数，采取多维参数搜索方式分别更新状态量及控制量，避免传统一维线搜索出现无解情况；进一步选取约束松弛项作为同伦参数，基于模型预测误差评估自适应调整离散网格，并构建考虑多约束的静态凸规划问题模型，同伦优化求取全程控制调整量，以实现全要素轨迹状态量高精度校正，提升算法收敛性。最终，通过数值仿真验证了本文方法的有效性。

Under complete terminal state constraints, the reentry gliding trajectory planning of suborbital reusable aircraft is short of convergence and accuracy. A high-precision solution algorithm combining sequential convex optimization and adaptive mesh refinement based homotopic constrained model predictive convex programming is proposed to achieve high-precision and reliable convergence in the reentry trajectory planning process under the conditions of weak initial value dependency. Firstly, several terminal constraints are relaxed, and the trust region is utilized as a soft constraint during the convex optimization process. An augmented index function is defined to reflect the linearization error and the degree of constraint violation. A multi-dimensional parameter search method is proposed to update the state and control variables separately, avoiding the situation of no solution in the basic line search. Further, constraint relaxation terms are selected as homotopy parameters, the discrete mesh is adjusted adaptively based on evaluation of model prediction deviation, and a static convex programming problem model is constructed considering multi-constraints. Then, the control adjustment quantity in the entire process is homotopically optimized to achieve high-precision correction of all-element trajectory state variables and improve algorithm convergence. Finally, the effectiveness of the proposed method is verified through numerical simulation.