为实现欠驱动条件下临近空间飞行器每个终端状态的准确控制，需突破机载运算能力有限的瓶颈，发展面向计算制导的高精度、高效率轨迹优化架构。如果离散点数量不足，现有基于偏差动力学的牛顿类轨迹优化（模型预测静态规划）方法存在较大的终端控制误差。受到现有准谱牛顿法启发，提出了一种基于实际动力学的准谱优化方法。与现有方法间接更新优化系数存在本质不同，该方法直接更新优化系数。而后，证明了该方法是一种牛顿法，并明确指出在多次迭代过程中，若不引入阻尼机制存在鲁棒性差的问题。面向牛顿法存在鲁棒性差和控制精度低的问题，融合模糊控制提出了一种自适应轨迹优化方法，避免了无阻尼方法初值敏感迭代直接发散的问题，提升了鲁棒性与精度。针对动态轨迹优化问题，仿真结果说明：与现有方法相比，提出方法在相同条件下减少了计算时间。

To achieve precise control of terminal states for near-space vehicles under underactuated conditions， it is necessary to overcome the limitations of onboard computational capability and develop a highly precise and efficient trajectory optimization framework in computational guidance. If the number of discretization points is too low， existing Newton-type trajectory optimization methods by deviation dynamic equations （Model Predictive Static Programming） have large terminal control errors. Motivated by current quasi-spectral Newton method， a quasi-spectral optimization method is proposed by actual dynamic equations. Unlike existing methods that update the optimization coefficient indirectly， this method updates the optimization coefficient directly. Additionally， it is proven that this method is a Newton optimization method， and it is clearly shown that when damping mechanism is not considered， this method suffers from poor robustness during multiple iterations. To address the poor robustness and low control accuracy in typical Newton methods， an adaptive trajectory optimization method is proposed with fuzzy control. This method avoids the divergence problem commonly encountered in damping-free methods with sensitive initial values， thereby enhancing both robustness and accuracy. Simulation results show that the proposed method achieves faster dynamic trajectory optimization than existing methods under identical conditions.