助推滑翔飞行器轨迹优化大部分以确定性的轨迹优化模型为前提，难以应对实际飞行场景中存在的多源不确定因素并导致规划出的轨迹结果鲁棒性不足。针对上述问题，本文首先构建了考虑参数不确定性因素影响的鲁棒轨迹优化模型，然后使用数据驱动的多项式混沌展开模型，将双层嵌套的不确定性轨迹优化问题转化为维数扩展的确定性轨迹优化问题，最后使用基于序列凸优化的确定性轨迹优化求解策略，实现对该维数扩展的确定性优化问题求解方法处理模型中的非线性动力学约束，获取优化后的轨迹曲线。由于所提方法是一种数据驱动的不确定性轨迹优化设计方法，无需已知不确定性因素的全概率分布信息，能处理随机参数存在相关性的不确定性轨迹优化问题，避免了错误的分布特性假设对优化结果的影响。由某款助推滑翔飞行器轨迹优化结果表明，所提方法能够降低多源不确定性因素对优化结果的影响，与现有助推滑翔飞行器不确定性轨迹优化方法相比具有明显的精度和计算效率优势。
Most trajectory planning for high-speed aircraft is based on deterministic trajectory optimization models, which struggle to handle the multi-source uncertainties present in actual flight scenarios, leading to insufficient robustness in the planned trajectories. To address this issue, this paper first constructs a robust trajectory optimization model. Then, a data-driven method is used to build a sparse polynomial chaos expansion surrogate model, thereby transforming the double loop nested uncertain trajectory optimization problem into a dimension-expanded deterministic trajectory optimization problem. Finally, a sequential convex optimization-based deterministic trajectory optimization solution strategy is employed to solve this dimension-expanded deterministic optimization problem and obtain the optimized trajectory. Since the proposed method is a data-driven uncertain trajectory optimization design approach, it does not require prior knowledge of the full probability distribution of uncertain variables, avoiding the impact of incorrect uncertainty assumptions on the optimization results. Trajectory optimization results of a specific boost-glide high-speed aircraft demonstrate that the proposed method can mitigate the influence of multi-source uncertainties on the optimization results. Moreover, it has advantages of considerable accuracy and computational efficency when compared with existing uncertain trajectory optimization methods for high-speed aircraft.
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