Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

  • A. Valdes
  • K. Khorasani
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed “High Level” FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the “High Level” FDI scheme and the DNN-based “Low Level” FDI scheme developed earlier by the authors, an “Integrated” DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.


fault detection and isolation dynamic neural networks formation flying pulsed plasma thrusters 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • A. Valdes
    • 1
  • K. Khorasani
    • 1
  1. 1.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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