针对火箭基组合循环（RBCC）高超声速飞行器上升段轨迹设计所具有的动力系统工作模态复杂、推力与飞行状态存在强耦合、模型强非线性、存在多种复杂约束限制和参数不确定性因素影响等典型特征，提出了一种基于非嵌入式混沌多项式、高斯求积法和序列凸优化的RBCC动力上升段鲁棒轨迹优化方法，以提高轨迹的抗干扰能力和过程可靠性。首先，构建了考虑参数不确定性的RBCC高超声速上升段鲁棒轨迹优化模型，并设计了基于高斯求积与非嵌入式混沌多项式的不确定性量化传播算法，从而将其转化为维数扩展的确定性轨迹优化问题；随后，基于凸优化理论对该问题进行凸化和离散，设计了一种基于序列凸优化算法的轨迹优化求解策略，以实现对该高维确定性问题的快速求解。某空基投放上升轨迹优化结果表明，基于所构建模型和轨迹优化方法可以有效地完成RBCC高超声速飞行器上升段鲁棒轨迹优化，优化结果符合RBCC动力系统工作特点；与传统确定性轨迹优化算法相比，所提方法能够有效降低随机干扰对上升段轨迹的影响，从而提升轨迹的可靠性与鲁棒性。

The ascent trajectory design for Rocket-Based Combined Cycle （RBCC） hypersonic vehicle has many typical characteristics， including complex power system working modes， strong coupling between thrust and flight state， highly nonlinear models， numerous complex constraints， parameter uncertainties， etc. In this paper， a robust trajectory optimization method for RBCC power ascent based on non-intrusive polynomial chaos， the Gaussian quadrature strategy， and sequential convex optimization is proposed to enhance the trajectory’s anti-interference ability and process reliability. Firstly， a robust trajectory optimization model for the RBCC hypersonic ascent， accounting for parameter uncertainties， is established. An uncertainty quantification propagation algorithm based on the Gaussian quadrature strategy and non-intrusive polynomial chaos is designed to transform the robust optimization model into a deterministic trajectory optimization problem with extended dimensions. Then， the extend model is convexified and discretized using convex optimization theory， and a trajectory optimization solution strategy based on sequential convex optimization algorithm is established to achieve a solution of this high-dimensional deterministic optimization problem. The optimaization results of a certain air-based vehicle’s ascent trajectory indicate that based on the constructed model and trajectory method， the robust trajectory optimization of the ascent phase of the RBCC hypersonic aircraft can be effectively completed， and the optimization results are in line with the working characteristics of the RBCC power system. Compared with the traditional deterministic trajectory optimization algorithm， the proposed method can effectively reduce the influence of random disturbances on the ascent trajectory， thereby improving the reliability and robustness of the trajectory.