Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission
This work addresses the need for precise propulsion modeling to improve guidance and control in lunar landing missions, representing an incremental advancement in domain-specific aerospace engineering.
The paper tackled the problem of accurately modeling throttleable engine dynamics for lunar landing missions, which involve complex non-linear systems, by developing a learning-based system identification approach validated with experimental results and used in closed-loop simulations.
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.