Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission
Suraj Kumar, Aditya Rallapalli, Bharat Kumar GVP
典型的登月任务涉及制动的多个阶段,以实现软着陆。 这些任务的推进系统配置包括节流发动机。 这种配置涉及复杂的互连液压,机械和气动组件,每个部件都表现出非线性动态特性。 推进动力学的精确建模对于分析下降过程中的闭环引导和控制方案至关重要。 本文介绍了一种基于学习的系统识别方法,用于利用从高保真推进模型获得的数据对节流发动机动力学进行建模。 开发的模型通过实验结果进行验证,并用于闭环引导和控制模拟。
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 sy...