基于扩散的多重打靶间接最优控制在燃料最优航天器轨迹生成中的应用

中文摘要 AI 辅助

扩散生成模型（DMs）已在控制问题，特别是机器人技术中找到了应用，其中DMs能够探索可能的控制解决方案。这些应用的一个关键缺点是缺乏最优性保证。这对于具有长时间跨度和bang-bang剖面的燃料最优航天器轨迹的潜在使用来说是一个问题。另一方面，间接最优控制方法确保显式满足必要条件，但对求解由此产生的哈密顿边值问题（HBVPs）所需的初始协态估计高度敏感。为了减轻这种敏感性并扩大HBVPs的收敛域，已经开发了使用平滑方法和延续的先进间接方法。我们提出了一种基于扩散的多重打靶间接控制方法，该方法将DMs的探索能力与间接方法相结合，以生成燃料最优航天器轨迹。我们在燃料最优的地球-火星低推力转移问题上将我们的方法与一种先进的间接方法进行了基准测试，显示出比基于随机协态初始化的先进间接方法更高的收敛鲁棒性。代码和可视化可在以下网址获取：此https URL。

英文摘要

Diffusion-based generative models (DMs) have found applications in control problems, and in particular robotics, where the DMs enable exploration of possible control solutions. A critical shortcoming of these applications is that they have lacked optimality guarantees. This is a problem for their potential use in fuel-optimal spacecraft trajectories that are characterized with long time-horizons and bang-bang profiles. Alternatively, indirect optimal control methods ensure explicit satisfaction of necessary conditions, but are highly sensitive to the initial costate estimation needed to solve the resulting Hamiltonian boundary-value problems (HBVPs). To alleviate this sensitivity and enlarge the convergence domain of HBVPs, advanced indirect methods have been developed that use smoothing approaches and continuation. We propose a diffusion-based multiple shooting indirect control method that combines the exploration capability of DMs with indirect method to generate fuel-optimal spacecraft trajectories. We benchmark our method against an advanced indirect method on a fuel-optimal Earth-Mars low-thrust transfer problem, showing higher convergence robustness than the advanced indirect method that is based on random costate initialization. Code and visualizations are available at https://saeidtafazzol.github.io/Diffusion_Indirect_Control/.

发表机构

• Auburn University（奥本大学）
• Princeton University（普林斯顿大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Submitted to IEEE Control Systems Letters (L-CSS) for possible publication