At DLR neural networks, as potential future controller for rocket engines, are studied. A neural network-based chamber pressure controller for a simplified cold gas thruster is presented and analyzed in simulation and experiment. The goal of the controller is twofold: It can track a trajectory with different changes of setpoints and it allows to set and control a wide variety of steady state chamber pressures. The neural network gets feeding line pressure measurement data as input and calculates valve positions as output values. The training phase of the controller is done with a reinforcement learning algorithm in an EcosimPro/ESPSS simulation, that is validated with data from the corresponding experimental set up. To increase the robustness and to allow a transfer from the simulation directly to the test facility domain randomization is applied. The controller is evaluated in simulations and experiment. It was found that – in the range of physically possible operation points – the controller achieves a constantly high reward which corresponds to a low error and a good control performance. In the simulation the controller was able to adjust all required set points with a steady state error of less than 0.1bar while retaining a small overshoot and an optimal settling time. It is found that the controller is also able to regulate all desired set points in the real experiment. A reference trajectory with different steps, linear and sinus changes in target pressure is tested in simulation and experiment. The controller was in both cases able to successfully follow the given trajectory.

中文翻译：

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基于强化学习的冷气推力室压力控制器的实验与仿真评估

德国航天中心正在研究神经网络作为未来火箭发动机的潜在控制器。提出了一种基于神经网络的简化冷气推进器腔室压力控制器，并通过仿真和实验进行了分析。控制器的目标是双重的：它可以跟踪设定点不同变化的轨迹，并且允许设置和控制各种稳态室压力。神经网络获取进料管线压力测量数据作为输入，并计算阀门位置作为输出值。控制器的训练阶段是通过 EcosimPro/ESPSS 模拟中的强化学习算法完成的，并使用相应实验设置的数据进行验证。为了提高鲁棒性并允许从模拟直接转移到测试设施，应用了域随机化。通过仿真和实验对控制器进行评估。结果发现，在物理上可能的操作点范围内，控制器获得了持续较高的回报，这对应于较低的误差和良好的控制性能。在仿真中，控制器能够调整所有所需的设定点，稳态误差小于 0.1bar，同时保持较小的超调和最佳稳定时间。发现控制器在实际实验中也能够调节所有所需的设定点。在模拟和实验中测试了目标压力具有不同步长、线性和正弦变化的参考轨迹。在这两种情况下，控制器都能够成功地遵循给定的轨迹。