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arXiv · 2006.11108

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Günther Waxenegger-Wilfing·Kai Dresia·Jan Christian Deeken·Michael Oschwald
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Abstract

Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. A quantitative comparison with carefully tuned open-loop sequences and PID controllers is included. The deep reinforcement learning controller achieves the highest performance and requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control. control.

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Günther Waxenegger-Wilfing, Kai Dresia, Jan Christian Deeken, Michael Oschwald. 2020-06-19. A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines. https://doi.org/10.1109/taes.2021.3074134

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