A neural network model relating extraction current characteristics with optical emission spectra for the purpose of a digital twin of miniaturized ion thrusters

Miniaturized ion thrusters are one of the most important candidates in the task of drag-free control for space-based gravitational wave detection, the thrust of which can be accurately tuned in principle by in-orbit monitoring and feedback control. This work investigates a neural network model (NNM) that can be used for real-time monitoring of the function that relates the grid voltage and the extraction current of a miniaturized ion thruster using optical emission spectroscopy. This model is developed as a component of an ion thruster's digital twin. A collisional-radiative model relates the plasma parameters in the discharge chamber of the thruster to the emission spectroscopy; an extraction current model relates the plasma parameters to the function that relates the grid voltage and extraction current. The NNM is trained based on the dataset produced by these models, and is examined by experimental results from a miniaturized ion thruster. It is found that the difference between the thrust predicted by the NNM and the experimental value is less than 6%. Discussions are given on further improvement of the NNM for accurate thrust control in space-based gravitational wave detection in the future.