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Data-physics coupled model for Ion propulsion system

  • Siyuan Ren
  • , Haibin Tang
  • , Junxue Ren*
  • , Haixu Guo
  • , Yibai Wang
  • , Zheng Wen
  • , Zongliang Li
  • , Fei Song
  • , Yizhuo Liu
  • , Fenglin Ding
  • *Corresponding author for this work
  • Beihang University
  • Key Laboratory of Precision Opto-Mechatronics Technology (Ministry of Education)
  • Beijing Key Laboratory of High Efficiency Spacecraft Propulsion Technology
  • China Aerospace Science and Technology Corporation
  • CAS - Beijing Institute of Control Engineering

Research output: Contribution to journalArticlepeer-review

Abstract

A novel grid acceleration current model for the ion propulsion system is designed, and a complete ion propulsion system model including power processing unit, gas feed system, discharge chamber, and grid system is established. This model enables time-series simulation of the performance parameters of the ion propulsion system. On this basis, combined with actual data and CNN-LSTM, a data-driven error compensation and prediction model is constructed, which can obtain results with higher fidelity and realize the prediction of future evolution trends. Finally, by integrating the system model and the CNN-LSTM error compensation model, a data-physics coupled model for the ion propulsion system is established, which can achieve high-precision prediction of key parameters such as beam current and acceleration current. In the calculation for the 1 kW operational condition of the LIPS-200 ion thruster with a 13 sccm Xe propellant flow rate, the system model calculates a discharge voltage of 36 V, a discharge current of 4 A, a beam current of 0.8 A, and a thrust of 37.7 mN, which are basically consistent with the actual measured results. On this basis, the data-physics coupled model further improves the result accuracy. In the 50-point prediction, the data-physics coupled model achieves RMSEs of approximately 1.6 × 10−3 A and 3.1 × 10−3 mA for the beam current and acceleration current, respectively. At the same time, it realizes the prediction of the variation trend of key parameters of the electric propulsion system, and shows relatively excellent performance compared with various prediction methods. The research results have certain engineering application value for the design optimization, on-orbit state prediction and long-term health management of the ion propulsion system, particularly exhibiting unique advantages in the ground-to-orbit equivalence of ion propulsion and the autonomous fault tolerance for deep space exploration missions.

Original languageEnglish
Pages (from-to)664-679
Number of pages16
JournalActa Astronautica
Volume246
DOIs
StatePublished - Sep 2026

Keywords

  • CNN-LSTM
  • Data-driven
  • Data-physics coupled model
  • Grid system
  • Ion propulsion system
  • System model

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