Event-triggered attitude maneuver control of spacecraft under angular velocity constraints

Abstract

Considering the spacecraft attitude system subject to angular velocity constraints, limited communication resources, the inaccurate moment of inertia, and external interference, a neural network-based adaptive event-triggered attitude maneuver control scheme is proposed. Specifically, the angular velocity constraint is first transformed into the performance boundary constraint based on the prescribed performance method, and then the equivalent error model of the attitude system is established using error transformation, which tactfully transforms the attitude maneuver control problem with angular velocity constraints into the state-bounded stability control problem of the unconstrained error system. Subsequently, by applying the radial basis function neural network, an adaptive online updating law is designed to approximate the uncertainty term caused by the unknown moment of inertia online. Meanwhile, considering the limited communication resources, a unified time-varying event-triggered mechanism is developed by establishing the explicit relationship between the trigger control signal and the real-time control one. The control command and the adaptive law are synchronously updated once the trigger condition is satisfied. By doing so, the frequent network signal transmissions between the controller and the actuator are reduced significantly. In addition, the time-varying term in the event-triggered mechanism strictly guarantees that no Zeno phenomenon occurs. The simulation results show that the proposed attitude control algorithm can achieve the specified attitude maneuver task with higher accuracy, stability, and robustness and reduce frequent control signal updates by about 97.50%, significantly reducing the on-board communication burden.