Measurement and Prediction of Micronewton Class Thrust of Electric Propulsion Based on the Torsional Pendulum and Machine Learning Technique

Accurate and direct measurement of thrust is the most fundamental requirement in the evaluation of electric thruster performance. This article performed the measurement and prediction of the micronewton class thrust of micro gridded ion thrusters (mu GITs) based on a novel design of torsional pendulum and high precision neural network models. The developed torsional pendulum adopts specially designed liquid metal connectors to eliminate the interference caused by the power supply cables and propellant feeding lines, realizing an accurate thrust measurement of radio frequency (RF)- and dc-mu GIT from micronewton level to millinewton level with a measuring range of 0-10 mN and a resolution of 6.4 mu N. Results show that the RF-mu GIT generates a thrust of 0.46-2.43 mN for the RF power from 100 to 120 W and the xenon flow rate from 0.75 to 1.50 sccm. The dc-mu GIT generates a thrust of 0.016-0.310 mN for the acceleration voltage from 700 to 1050 V and the flow rate from 0.2 to 2.0 sccm. Both the artificial neural network (ANN) and the radial basis function neural network (RBF-NN) are adopted to predict the relationship between the thrust and input parameters of RF-mu GIT. The predicted thrusts by ANN deviate about 10% from the measured data, and the maximum relative deviation using RBF-NN model is about 2%. Furthermore, the RBF-NN model is used to obtain the distribution maps of thrust and specific impulse under 600 different working conditions and provides a solution for intelligent regulation of thruster performance. For the first time, the combination of thrust measurement and machine learning (ML) provides a new approach for the fast performance evaluation, prediction and regulation of electric thrusters.