Preliminary Investigation of Robust Reinforcement Learning for Control of an Existing Green Propellant Thruster

Abstract

Advanced engine control is an important requirement for the efficient operation of future reusable engines, facilitating a safer and more economical engine operation. For this reason, modern control strategies are extensively studied in recent years, mainly through the use of simulation environments. An important development step is to test the performance and robustness of the control algorithm at real test benches. The present paper describes the first steps towards the use of an reinforcement learning based controller on a N2O / C2H6 22 N green propellant thruster. The control objectives are given by regulating the mixture ratio and combustion pressure. The existing test bench is modelled in EcosimPro / ESPSS. Based on the simulation model deep reinforcement learning is used to train the controller and domain randomization is used to increase the robustness. The overall goal is to transfer the controller from the simulation model to the real test bench. Finally, preliminary experiments demonstrate the basic functionality of reinforcement learning based controllers for real rocket propulsion systems.