This paper proposes a neural network and event-based fault-tolerant control scheme for spacecraft attitude stabilization in the presence of lumped disturbances, which consists of space disturbances, inertia uncertainties, and actuator faults. A neuro-adaptive estimator is employed to approximate the lumped disturbances, with the help of its powerful adaptive estimation capability of approximating any unknown smooth nonlinear function with arbitrary accuracy. The estimation is then utilized to formulate an integrated event-based dual-channel control scheme that can both guarantee the system's convergence and ensure the event triggering sequence possessing no-Zeno behavior simultaneously. The proposed control scheme provides a new and straightforward way for spacecraft attitude control to deal with lumped disturbances while requiring a low actuator updating frequency, thus saves on-board communication resources. Numerical simulations show the effectiveness of the algorithms.

本文提出了一种基于神经网络和基于事件的容错控制方案，用于在存在集中扰动的情况下稳定航天器的姿态，该方案包括空间扰动，惯性不确定性和执行器故障。利用神经自适应估计器来近似集总扰动，借助其强大的自适应估计能力，可以以任意精度近似任何未知的平滑非线性函数。然后，将估计值用于制定基于事件的集成双通道控制方案，该方案既可以保证系统的收敛性，又可以确保事件触发序列同时具有no-Zeno行为。所提出的控制方案为航天器姿态控制提供了一种新颖而直接的方式来处理集总干扰，同时需要较低的执行器更新频率，从而节省了机载通信资源。数值仿真表明了算法的有效性。