Fault Diagnosis for Satellite Sensors and Actuators using Nonlinear Geometric Approach and Adaptive Observers

This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that aid detection, isolation, and estimation of possible actuator and sensor faults. The adaptive observers do not need a priori information about fault internal models. A nonlinear geometric approach is used to avoid that aerodynamic disturbance torques have unwanted influence on the fault estimates. An augmented high-fidelity spacecraft model is exploited during design and validation to replicate faults. This simulation model includes disturbance torques as experienced in low Earth orbits. This paper includes an analysis to assess robustness properties of the method with respect to parameter uncertainties and disturbances. The results document the efficacy of the suggested methodology.This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that aid detection, isolation, and estimation of possible actuator and sensor faults. The adaptive observers do not need a priori information about fault internal models. A nonlinear geometric approach is used to avoid that aerodynamic disturbance torques have unwanted influence on the fault estimates. An augmented high-fidelity spacecraft model is exploited during design and validation to replicate faults. This simulation model includes disturbance torques as experienced in low Earth orbits. This paper includes an analysis to assess robustness properties of the method with respect to parameter uncertainties and disturbances. The results document the efficacy of the suggested methodology

M.: Model-based robust fault diagnosis for satellite control systems using learning and sliding mode approaches

Abstract — In this paper, our recent work on robust modelbased 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 I