An overview of robust model-based fault diagnosis for satellite systems using sliding mode and learning approaches

In this paper, our work on robust fault diagnosis (FD) for satellite control systems using sliding mode and learning approaches are summarized. Firstly, a variety of nonlinear mathematical models for satellites are described and analyzed for the purpose of fault diagnosis. Then, fault diagnostic sliding mode observer with time-varying gains is presented and analyzed. Two classes of learning estimators are integrated with the sliding mode observer to construct robust fault diagnosis schemes, which are investigated as well. Finally, conclusions and future work on the health monitoring and fault diagnosis for satellite systems are proposed.