Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission

TL;DR

This paper introduces a learning-based system identification method to accurately model throttleable engine dynamics for lunar landing, enabling improved guidance and control during descent.

Contribution

It presents a novel data-driven modeling approach for complex engine dynamics, validated with high-fidelity simulation data and applied to lunar landing control schemes.

Findings

Model accurately captures engine dynamics

Validated with high-fidelity simulation data

Enhances guidance and control accuracy

Abstract

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.