Neural Network Prediction of Discharge Current using Plume Shape and Operational Parameters in Hall Thrusters

• Kawazu Masato

  連絡先著者（Corresponding author） ：[e-mail] 九州大学大学院総合理工学府先端エネルギー理工学専攻

• Fuchigami Hirotaka

  九州大学大学院総合理工学府先端エネルギー理工学専攻

• Yamamoto Naoji

  九州大学大学院総合理工学研究院エネルギー科学部門

• Tamida Taichiro

  三菱電機株式会社先端技術総合研究所駆動制御システム技術部放電システムグループ

Bibliographic Information

Other Title

• プルーム画像を用いたニューラルネットワークによる ホールスラスタの放電電流予測

Abstract

A neural network for prediction of discharge current, which shows nonlinearity and hysteresis dependent on coil current, has been developed to build auto control system of Hall thrusters. The prediction accuracy dependence on training data sets composed of operational parameters (previous work), 250 images of plume shape and both, operational parameters and images, are investigated. The network using only plume images can describe the non-linear mode hop jump and hysteresis that the network using only operational parameters cannot describe. The predicted discharge current, however, is fluctuated up and down, while that observed in experiment shows smooth curve. The prediction using both operating parameters and plume images as the training data, can describe mode hop jump and hysteresis with 0.8% difference between prediction current and that observed in experiment.

Journal

• AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES

  AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 20 (0), 47-51, 2021

  THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES

Keywords

• Propulsion
• Hall Thruster
• Neural Network
• Auto Controlling

Details

• CRID: 1390005966211484416
• NII Article ID: 130008002177
• DOI: 10.2322/astj.20.47
• ISSN: 18840477
• HANDLE: 2324/4377921
• Web Site: https://www.jstage.jst.go.jp/article/astj/20/0/20_JSSAS-D-20-00027/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed