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Fault detection in reaction wheel of a satellite using observer-based dynamic neural networks. (English) Zbl 1084.68685

Wang, Jun (ed.) et al., Advances in neural networks – ISNN 2005. Second international symposium on neural networks, Chongqing, China, May 30 – June 1, 2005. Proceedings, Part III. Berlin: Springer (ISBN 3-540-25914-7/pbk). Lecture Notes in Computer Science 3498, 584-590 (2005).
Summary: 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.
For the entire collection see [Zbl 1073.68015].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
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