WEN Xin, WANG Jiayi, LI Xin. A Feed-Forward Wavelet Neural Network Adaptive Observer-Based Fault Detection Technique for Spacecraft Attitude Control Systems[J]. Chinese Journal of Electronics, 2018, 27(1): 102-108. doi: 10.1049/cje.2017.11.010
Citation: WEN Xin, WANG Jiayi, LI Xin. A Feed-Forward Wavelet Neural Network Adaptive Observer-Based Fault Detection Technique for Spacecraft Attitude Control Systems[J]. Chinese Journal of Electronics, 2018, 27(1): 102-108. doi: 10.1049/cje.2017.11.010

A Feed-Forward Wavelet Neural Network Adaptive Observer-Based Fault Detection Technique for Spacecraft Attitude Control Systems

doi: 10.1049/cje.2017.11.010
Funds:  This work is supported by the National Natural Science Foundation of China (No.61571309, No.61101161).
  • Received Date: 2015-11-02
  • Rev Recd Date: 2016-07-28
  • Publish Date: 2018-01-10
  • This paper presents a novel neural network-based fault detection technique applicable to a class of nonlinear systems. The adaptive observer was designed for fault detection based on a single hidden layer feed-forward wavelet neural network. In order to guarantee network convergence, the network weights are updated according to a modified back-propagation algorithm, and the Lyapunov function is introduced to ensure stability. The proposed fault detection scheme was tested on the actuators of a typical spacecraft attitude control system, and the results demonstrated the effectiveness and feasibility of the proposed observer in detecting nonlinear system failure.
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