Fault Prediction of Satellite Attitude Control System Based on Neural Network

doi:10.16182/j.issn1004731x.joss.19-FZ0351E

Journal of System Simulation ›› 2019, Vol. 31 ›› Issue (11): 2499-2508.doi: 10.16182/j.issn1004731x.joss.19-FZ0351E

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Fault Prediction of Satellite Attitude Control System Based on Neural Network

Meng Xiaofan¹, Song Hua^1,2

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Science and Technology on Space Intelligent Control Laboratory, Beijing 100190, China

Received:2019-05-30 Revised:2019-07-19 Online:2019-11-10 Published:2019-12-13
Supported by:
Science and Technology on Space Intelligent Control Laboratory (HTKJ2019KL502010)

Abstract

Abstract: A new method based on BP neural network(BPNN), wavelet neural network(WNN) and wavelet decomposition-LSTM(wLSTM) network is proposed for predicting faults in the satellite attitude control system. Normal satellite attitude data is used to train BPNN which is used as the standard model of satellite attitude control system. The real-time attitude residuals is obtained by subtracting the BPNN output attitude angle from the real-time data of satellite attitude. The time series of the residuals are used to build WNN and wLSTM models to predict the faults of satellite attitude control system. A conclusion is given according to comparing the WNN and wLSTM that both the fault prediction methods can precisely predict the fault and the wLSTM model predicts more accurately because LSTM network can selectively retain the characteristics of input data. At the same time, it also provides a novel method for the prediction of complex system.

Key words: fault prediction, satellite attitude control system, BPNN, WNN, wLSTM

CLC Number:

V249.32

Meng Xiaofan, Song Hua. Fault Prediction of Satellite Attitude Control System Based on Neural Network[J]. Journal of System Simulation, 2019, 31(11): 2499-2508.

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Fault Prediction of Satellite Attitude Control System Based on Neural Network

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