System and method for real-time machine learning control of plasma osciallations in hall thrusters

A non-transitory computer-readable medium storing instructions that, when executed by a field programmable gate array (FPGA), cause the FPGA to perform operations comprising:

• receiving digitized discharge current measurement data from an analog-to-digital converter, wherein the discharge current measurement data corresponds to discharge current oscillations of a Hall effect thruster;
• executing a neural network model stored on the FPGA to predict discharge current dynamics based on the digitized discharge current measurement data, wherein the neural network model has been trained on Hall effect thruster discharge data;
• computing a control voltage output based on the predicted discharge current dynamics to reduce breathing mode oscillations in the Hall effect thruster;
• and
• outputting the control voltage output to a digital-to-analog converter for conversion to an analog control signal for modulating a discharge voltage of the Hall effect thruster.

A method for controlling plasma oscillations in a Hall effect thruster, comprising:

• measuring a discharge current of the Hall effect thruster using a current sensor to generate discharge current measurement data;
• digitizing the discharge current measurement data using an analog-to-digital converter;
• processing the digitized discharge current measurement data using a neural network model executed on a field programmable gate array (FPGA), wherein the neural network model predicts discharge current dynamics;
• computing a control voltage output based on the predicted discharge current dynamics using the FPGA;
• converting the control voltage output to an analog control signal using a digital-to-analog converter;
• and
• applying the analog control signal to modulate a discharge voltage of the Hall effect thruster to reduce breathing mode oscillations.