Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

P. Baldi, Mogens Blanke, P. Castaldi, N. Mimmo, S. Simani

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Abstract

This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume49
Issue number17
Pages (from-to)432–437
ISSN2405-8963
DOIs
Publication statusPublished - 2016
Event20th IFAC Symposium on Automatic Control in Aerospace - Sherbrooke, Quebec, Canada
Duration: 21 Aug 201625 Aug 2016

Conference

Conference20th IFAC Symposium on Automatic Control in Aerospace
CountryCanada
CitySherbrooke, Quebec
Period21/08/201625/08/2016

Keywords

  • Fault diagnosis
  • Geometric approaches
  • Neural networks
  • Actuators
  • sensors
  • Satellite control applications

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