用深度学习模拟器优化星舰翻滚着陆轨迹，无需线性化。
Optimization of Flip-Landing Trajectories for Starship based on a Deep Learned Simulator
- 用神经网络替代复杂气动计算，实现端到端可微优化。
- 在不线性化条件下完成高非线性机动的轨迹优化。
- 适合航天器轨迹设计与智能制导研究者参考。
我们提出一种可微分优化框架，用于可重复使用航天器（以星舰为例）的翻滚着陆轨迹设计。基于高保真流体动力学数据训练的深度神经网络代理模型，可预测气动力和力矩，并与可微刚体动力学求解器紧密耦合。该框架实现了无需线性化或凸松弛的端到端梯度优化。同时处理执行器限制与终端着陆约束，生成物理一致的最优控制序列。采用标准自动微分与神经微分方程支持长时域滚动。结果表明该框架在建模与优化复杂非线性机动方面具有显著有效性。本工作为未来扩展至非定常气动、喷流干扰及智能制导设计奠定了基础。
原文摘要 · Abstract (English)
We propose a differentiable optimization framework for flip-and-landing trajectory design of reusable spacecraft, exemplified by the Starship vehicle. A deep neural network surrogate, trained on high-fidelity CFD data, predicts aerodynamic forces and moments, and is tightly coupled with a differentiable rigid-body dynamics solver. This enables end-to-end gradient-based trajectory optimization without linearization or convex relaxation. The framework handles actuator limits and terminal landing constraints, producing physically consistent, optimized control sequences. Both standard automatic differentiation and Neural ODEs are applied to support long-horizon rollouts. Results demonstrate the framework's effectiveness in modeling and optimizing complex maneuvers with high nonlinearities. This work lays the groundwork for future extensions involving unsteady aerodynamics, plume interactions, and intelligent guidance design.
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