角速度受限下航天器姿态机动事件触发控制

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

针对角速度受限和通信资源有限的航天器姿态系统,考虑转动惯量难以精确获得并且存在外部干扰的情况,提出一种基于神经网络的自适应事件触发姿态机动控制算法.该算法首先基于预设性能技术,将角速度受限约束转换为性能边界约束,进一步通过误差转换,建立姿态系统的等效误差模型,将存在角速度约束的航天器姿态系统机动控制问题巧妙地转化为无约束误差系统的状态有界稳定控制问题;然后,选用径向基神经网络设计自适应律,在线逼近系统中由未知转动惯量带来的不确定性项.与此同时,考虑到星载通信资源有限的问题,通过建立触发控制信号与实时控制信号之间的显式关系,设计一个统一的时变事件触发机制,当触发条件满足时同步更新控制器和自适应律,大大减少了控制器和执行器之间频繁的网络信号传输.此外,触发机制中时变项的引入严格保证了触发控制无Zeno现象发生.最后,仿真结果表明了所提出的姿态控制算法不但能够保证航天器高精度、高稳定度、高鲁棒性地完成指定姿态机动任务,而且可以减少大约97.50%的控制信号更新,这大大缓解了航天器的通信负担.

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.