Open AccessA Feed‐Forward Wavelet Neural Network Adaptive Observer‐Based Fault Detection Technique for Spacecraft Attitude Control Systems
Author(s) -
Xin WEN,
WANG Jiayi,
LI Xin
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.11.010
Subject(s) - control theory (sociology) , computer science , fault detection and isolation , observer (physics) , artificial neural network , lyapunov function , nonlinear system , convergence (economics) , fault (geology) , spacecraft , actuator , control engineering , artificial intelligence , control (management) , engineering , physics , quantum mechanics , aerospace engineering , seismology , geology , economics , economic growth
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.