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

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

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The issue of active attitude fault-tolerant stabilization control for spacecrafts subject to actuator faults, Acrylic Magnet inertia uncertainty, and external disturbances is investigated in this paper.To robustly and accurately reconstruct actuator faults, a novel mixed learning observer (MLO) is explored by combining the iterative learning algorithm and the repetitive learning algorithm.Moreover, to guarantee robust spacecraft attitude fault-tolerant stabilization, by synthesizing the mixed learning algorithm with the sliding mode controller, a novel mixed learning sliding-mode controller (MLSMC) is designed based on the separation principle, in which the mixed learning algorithm is used to 2 Piece Outdoor Sofa Sectional with Chair update composite disturbances online, including fault errors, inertia uncertainty, and external disturbances.Finally, a numerical example is provided to demonstrate the effectiveness and superiority of our proposed spacecraft attitude fault-tolerant stabilization control approach.