Event-driven adaptive fault-tolerant tracking control for uncertain mechanical systems with application to flexible spacecraft
Caisheng Wei, Yuxin Liao, Wenxiong Xi, Zeyang Yin, Jianjun Luo
Abstract
An event-driven neural network–based fault-tolerant tracking control scheme is investigated for uncertain mechanical systems with performance guaranteed in the presence of unknown actuator faults and external disturbances. Compared with the existing works, the primary advantage is that the detections and identifications of actuator faults are not required, whereas the convergence rate and tracking accuracy can be guaranteed a priori by constructing an adaptive tracking controller with a few aperiodic updates. Moreover, by using the norm-bounding skill, only two adaptive parameters are needed to update online, which dramatically decreases the complexity of the corresponding adaptive schemes. Finally, applications to the attitude stabilization and tracking control of the flexible spacecraft are used to validate the effectiveness of the proposed control scheme.