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

Miniaturized ion thruster is an important candidate for drag-free control of space-based gravitational wave detection. Its thrust can be accurately tuned through in-orbit monitoring and feedback controlling. Recently, we developed a neural network model for digital twin of miniaturized ion thruster. We would like to make a detailed presentation of our work to the community of Metrology, since it explores a new routine of measurement. This work reports a neural network model (NNM) which can provide real-time monitoring of the function relating the extraction current and the grid voltage of miniaturized ion thruster by optical emission spectroscopy. What is more important, it provides in-orbit measurement for important characteristics of plasma propulsion systems. 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 relating grid voltage and extraction current. The NNM is trained based on a dataset enhanced by these models, and is examined by experimental results. 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.