Rocket Engine Control with Neural Networks: Experimental Results of the LUMEN Turbopump Test Campaign

Kurzfassung

Advanced control algorithms will change the way we operate liquid propellant rocket engines in the future. One approach is to use neural networks as the control policy, which are trained in simulation with deep reinforcement learning. Neural network controllers can easily deal with multiple high-level control goals such as maximum efficiency, long fatigue-life, or mechanical and thermodynamic constraints. Within its technological road map towards cost-efficient and reusable engines, the German Aerospace Center (DLR) is developing an advanced engine controller for an expander-bleed engine. In this work, we design a neural network controller for the LUMEN turbopump and test the control performance at our test bench, tackling the challenges of real-world systems.