Nonlinear Control of an Expander-Bleed Rocket Engine using Reinforcement Learning

Abstract

Intelligent engine control could be one of the most important innovations in the development of future reusable engines, facilitating a safer and more eco nomical engine operation. In this work, we investigate the closed-loop control of the LUMEN expander-bleed engine by combining machine learning with a transient simulation environment. The controller can dynamically change the set-point of the engine between a chamber pressure of 40 bar to 80 bar by adjusting up to six flow control valves while maintaining several boundary conditions at any given time.