Miniaturized ion thrusters are one of the most important candidates in the task of drag-free control for space-based gravitational wave detection, whose thrust can be accurately tuned in principle by in-orbit monitoring and feedback controlling. This work investigates a neural network model (NNM) which can be used for real-time monitoring of the function relating the grid voltage and the extraction current of a miniaturized ion thruster by 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 relating grid voltage and extraction current. The neural network model 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.

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