Event-triggered neuroadaptive control for postcapture spacecraft with ultralow-frequency actuator updates

This paper investigates an event-triggered neuroadaptive control approach for postcapture flexible spacecraft with guaranteed prespecified tracking performance in the presence of unknown inertial properties, actuator constraints, and external space perturbations. By employing the minimum-learning parameter technique into the neural proportional integral-like controller, only two adaptive parameters are required to update online, which completely avoids the tedious inertial parameter identifications and dramatically reduces the complexity of controller in the meanwhile. Compared with existing works, the primary advantage of the proposed attitude control approach is that the actuator updates are determined by the prescribed event-based conditions in an aperiodic way rather than a periodic one, which greatly reduces the actuator updates. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed approach in terms of attitude stabilization and tracking for the postcapture flexible spacecraft.