The difficulty in identifying the faulted sensors and actuators of satellite Attitude Control Systems (ACS) resides in the spreading of faults in the closed loop. The deep forest algorithm is introduced in this study to build a fault prediction model to achieve the isolation of the sensor faults and actuator faults. After collecting healthy ACS telemetry data to group a training set according to the dynamic characteristics of ACS, we apply appropriate feature selection and extraction methods to the training set, obtaining the features of both the sensor faults and actuator faults. The deep forest algorithm, with its strong generalization ability, is then used to learn and identify fault information, thereby establishing a fault prediction model to realize the recognition of actuator and sensor faults. The results of the semi-physical simulation indicate that the proposed method can identify the faults of sensors and actuators effectively in the presence of many uncertain factors, such as the interference moments of air bearing testbeds, unknown moments of the inertia of satellites, nonlinear characteristics of flywheels and fault propagation in the closed loop of ACS.
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