基于可微物理的可重复使用运载火箭饱和感知鲁棒轨迹优化

中文摘要 AI 辅助

可重复使用运载火箭的大攻角翻转机动对鲁棒轨迹优化提出了重大挑战，因其具有高度非线性动力学、气动不确定性和执行器饱和的综合影响。本文提出了一个用于饱和感知鲁棒轨迹优化的可微物理框架。核心是开发了一种可微粒子管控制（DPTC）方案，通过基于集合的分布整形策略优化不确定性演化。状态不确定性由拉格朗日粒子集合表示，硬执行器投影算子直接嵌入计算图，通过端到端反向传播实现标称前馈轨迹和时变反馈策略的联合优化。通过与基于自动微分的逐次凸化（AD-SCvx）基线结合传统协方差转向反馈策略进行对比评估。六自由度蒙特卡罗模拟表明，基线虽能实现标称燃料最优解，但在气动干扰下其无约束反馈公式易受执行器饱和影响，导致闭环鲁棒性下降。相比之下，所提出的DPTC框架通过放宽空间跟踪以保留关键控制权，主动进行约束感知性能权衡。这些结果表明，将可微物理与基于集合的优化相结合，为高度受限的航空航天飞行系统中的鲁棒制导提供了一个有效且实用的框架。

英文摘要

The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation. This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization. At its core, a Differentiable Particle Tube Control (DPTC) scheme is developed to optimize uncertainty evolution through an ensemble-based distribution shaping strategy. State uncertainty is represented by a Lagrangian particle ensemble, while hard actuator projection operators are embedded directly into the computational graph, enabling the joint optimization of the nominal feedforward trajectory and a time-varying feedback policy via end-to-end backpropagation. The proposed framework is evaluated against an automatic differentiation-based Successive Convexification (AD-SCvx) baseline combined with a conventional covariance steering feedback strategy. Six-degree-of-freedom Monte Carlo simulations demonstrate that, although the baseline achieves nominal fuel-optimal solutions, its unconstrained feedback formulation becomes susceptible to actuator saturation under aerodynamic disturbances, leading to degraded closed-loop robustness. In contrast, the proposed DPTC framework proactively performs a constraint-aware performance trade-off by relaxing spatial tracking to preserve critical control authority. These results demonstrate that integrating differentiable physics with ensemble-based optimization provides an effective and practical framework for robust guidance in highly constrained aerospace flight systems.

发表机构

• Beijing Institute of Aeronautical Systems Engineering（北京航空航天系统工程研究所）

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