JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 4    Issue : 10    Date : October 2009

Model-based Robust Fault Diagnosis for Satellite Control Systems Using Learning and Sliding
Mode Approaches
Qing Wu and Mehrdad Saif
Page(s): 1022-1032
Full Text:
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Abstract
In this paper, our recent work on robust model-based fault diagnosis (FD) for several satellite
control systems using learning and sliding mode approaches are summarized. Firstly, a variety of
nonlinear mathematical models for these satellite control systems are described and analyzed for
the purpose of fault diagnosis. These satellite control systems are classified into two classes of
nonlinear dynamical systems. Then, several fault diagnostic observers using sliding mode and
learning approaches are presented. Sliding mode with time-varying switching gains, second order
sliding mode, and high order sliding mode differentiators are respectively used in the proposed
diagnostic observers to deal with modeling uncertainties. Neural model-based and iterative
learning algorithms-based online learning estimators are respectively used in the diagnostic
observers for the purpose of isolating and estimating faults. Finally, conclusions and future work on
the health monitoring and fault diagnosis for satellite control systems are provided.

Index Terms
fault diagnosis, observer, sliding mode, learning, satellite control systems