Fault detection in reaction wheel of a satellite using observer-based dynamic neural networks

  • Authors:

  • Zhongqi Li;Liying Ma;Khashayar Khorasani

  • Affiliations:

  • Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

This paper presents a methodology for the actuator fault detection in the satellite's attitude control system (ACS) by using a dynamic neural network based observer. In this methodology, a neural network is used to model a nonlinear dynamical system. After training, the neural network, it can give very accurate estimation of the attitude positions of the satellite. The difference between the actual and the estimated outputs is used as a residual error for fault detection. The simulation results show advantages of this method as compared to the method based on a generalized Luenberger linear observer.