用数据驱动方法建模月球着陆的可调推力发动机动态特性。
Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission
- 基于高保真数据，用学习方法识别发动机非线性动态。
- 模型经实验验证，可用于闭环制导与控制仿真。
- 适合从事月球着陆器动力系统设计的研究者。
典型月球着陆任务需经历多个减速阶段以实现软着陆。此类任务的推进系统采用可调推力发动机，其配置包含复杂的液压、机械和气动部件，均表现出非线性动态特性。精确建模推进系统动态对分析下降过程中的闭环制导与控制方案至关重要。本文提出一种基于学习的系统辨识方法，利用高保真推进模型生成的数据，对可调推力发动机动态进行建模。所构建模型经实验结果验证，并应用于闭环制导与控制仿真中。
原文摘要 · Abstract (English)
Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations.
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