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

WEN Xin; WANG Jiayi; LI Xin

doi:10.1049/cje.2017.11.010

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WEN Xin, WANG Jiayi, LI Xin, “A Feed-Forward Wavelet Neural Network Adaptive Observer-Based Fault Detection Technique for Spacecraft Attitude Control Systems,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 102-108, 2018, 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,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 102-108, 2018, doi: 10.1049/cje.2017.11.010

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

doi: 10.1049/cje.2017.11.010

1.
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2.
Shanghai Aerospace Control Technology Institute, Shanghai 201109, China;
3.
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

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

Abstract

Abstract

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.
- Single hidden layer feed-forward wavelet neural network,
- Fault detection,
- Nonlinear system,
- Adaptive observer

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References(18)

References

C. Gao, Q. Zhao and G. Duan, "Robust actuator fault diagnosis scheme for satellite attitude control systems", Journal of the Franklin Institute, Vol.350, No.9, pp.2560-2580, 2013.

J. Zhang, A.K. Swain and S.K. Nguang, "Robust sensor fault estimation scheme for satellite attitude control systems", Journal of the Franklin Institute, Vol.350, No.9, pp.2581-2604, 2013.

Z. Wang, M. Zhang and Y. Shen, "Actuator fault detection and isolation for the attitude control system of satellite", Journal of Harbin Institute of Technology, Vol.45, No.2, pp.72-76, 2013.

C. Bonivento, A. Isidoria, L. Marconia, et al., "Implicit faulttolerant control:Application to induction motors", Automatica, Vol.40, No.3, pp.355-371, 2004.

C.P. Tan and C. Edwards, "Sliding mode observers for robust detection and reconstruction of actuator and sensor faults", International Journal of Robust and Nonlinear Control, Vol.13, No.5, pp. 443-463, 2003.

D. Blake and M. Brown, "Simultaneous, multiplicative actuator and sensor fault estimation using fuzzy observers", Proc. of IEEE International Fuzzy Systems Conference, Kos, Greece, pp.408-414, 2007.

J. Wang, H. Peng, M. Tu, et al., "A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms", Chinese Journal of Electronics, Vol.25, No.2, pp.320-327, 2016.

M. Tu, J. Wang, H. Peng, et al., "Application of adaptive fuzzy spiking neural P systems in fault diagnosis of power systems", Chinese Journal of Electronics, Vol.23, No.1, pp.87-92, 2014.

H.A. Talebi, K. Khorasani and S. Tafazoli, "A recurrent neuralnetwork-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellites attitude control subsystem", IEEE Transactions on Neural Networks, Vol.20, No.1, pp.45-60, 2009.

Z. Du, B. Fan, J. Chi, et al., "Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks", Energy and Buildings, Vol.72, No.2, pp.157-166, 2014.

X. Wen, X.W. Zhang and W. Zhang, "Adaptive observer based on HBF neural networks", Acta Electronica Sinica, Vol.43, No.7, pp.1315-1319, 2015. (In Chinese)

V. Sadough, K. Khorasani and N. Meskin, "Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach", Information Sciences, Vol.259, No.3, pp.234-251, 2014.

A.K. Alexandridis and A.D. Zapranis, "Wavelet neural networks:A practical guide", Neural Networks, Vol.42, No.1, pp.1-27, 2013.

D. Costarelli and R. Spigler, "Solving Volterra integral equations of the second kind by sigmoidal functions approximation", Journal of Integral Equations and Applications, Vol.25, No.2, pp.193-222, 2013.

W.Y. Dong, H. Ding, X.S. Dong, et al., "An adaptive wavelet threshold de-nosing both in low and high frequency domains", Acta Electronica Sinica, Vol.43, No.12, pp.2374-2380, 2015. (In Chinese)

W.H. Yang and B.J. Zhou, "Analysis of multiple reference consistency check on LAAS ground facility", Acta Electronica Sinica, Vol.34, No.3, pp.469-471, 2006.(in chinese)

L.L. Show, J.C. Juang and C.D. Yang, "Nonlinear h∞ robust control for satellite large angle attitude maneuvers", Proc. of IEEE American Control Conference, Arlington, Virginia, USA, pp.1357-1362, 2001.

H. Wang and S. Daley, "Actuator fault diagnosis:An adaptive observer-based technique", IEEE Transactions on Automatic Control, Vol.41, No.7, pp.1073-1078, 1996.

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