Machine Learning Based Combustion Chamber Pressure and Mixture Ratio Controller - Simulative and Experimental Evaluation of Control Performance

Kurzfassung

A combustion chamber pressure- and mixture ratio-controller based on deep reinforcement learning methods is under development at German Aerospace Center - DLR. Training and implementation of the controller is described. The performance of this controller is evaluated in simulation and experimental tests. Application use case is a 22 N nitrous oxide/ethane thruster that can be operated in a wide range of operation points. Steady state and trajectory-following performance is measured via mean squared error, standard deviation, overshot and settling time evaluation in simulation and experiment. For steady state set-point control the controller shows good performance within the trained operation points in simulation and experiment. Root mean squared error of chamber pressure and mixture ratio is below 0.5 bar and 1 respectively for all physically achievable set-points. Control performance in simulation is better than at the real system. Trajectory-following performance in simulation is convincing, showing nearly no offset to the target values, whereas in the experimental evaluation deviations and fluctuations occur, indicating the need for further optimization when transferring the controller from simulation to the real system.