Spacecraft Attitude Stabilization Control with Fault-Tolerant Capability via a Mixed Learning Algorithm

The issue of active attitude fault-tolerant stabilization control for spacecrafts subject to actuator faults, inertia uncertainty, and external disturbances is investigated in this paper. To robustly accurately reconstruct a novel mixed learning observer (MLO) explored by combining the iterative algorithm repetitive algorithm. Moreover, guarantee robust spacecraft stabilization, synthesizing with sliding mode controller, sliding-mode controller (MLSMC) designed based on separation principle, which used update composite online, including fault errors, disturbances. Finally, numerical example provided demonstrate effectiveness superiority our proposed approach.