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

• Published in: Applied sciences Vol. 13; no. 16; p. 9415
• Main Authors: Wang, Jihe, Jia, Qingxian, Yu, Dan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Actuators; Algorithms; Controllers; Data mining; Design; Fault tolerance; fault-tolerant control; learning sliding-mode control; mixed learning observer; Neural networks; Space ships; Space vehicles; spacecraft attitude stabilization
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13169415

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 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 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.