In this paper, we focus on solving the problems of inertia-free attitude tracking control for spacecraft subject to external disturbance, unknown inertial parameters, and actuator faults. The robust control architecture is designed by using the rotation matrix and neural networks. In the presence of external disturbance and parametric uncertainties, a fault-tolerant control (FTC) scheme synthesized with the minimum-learning-parameter (MLP) algorithm is proposed to improve the reliability of the system when unknown actuator faults occur. These methods are developed based on backstepping to ensure that finite-time convergence is achievable for the entire closed-loop system states with low computational complexity. The validity and advantage of the designed controllers are highlighted by using Lyapunov-based analysis. Finally, the simulation results demonstrate the satisfactory performance of the developed controllers.

中文翻译：

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基于神经网络的航天器无退绕有限时间容错控制

在本文中，我们着重解决航天器在受到外部干扰，惯性参数未知和执行器故障的情况下的无惯性姿态跟踪控制问题。通过使用旋转矩阵和神经网络来设计鲁棒的控制架构。在存在外部干扰和参数不确定性的情况下，提出了一种结合最小学习参数（MLP）算法的容错控制（FTC）方案，以提高未知执行器故障时系统的可靠性。这些方法是基于反推法开发的，以确保对于整个闭环系统状态都可以以低计算复杂度实现有限时间收敛。通过使用基于Lyapunov的分析，突出了所设计控制器的有效性和优势。最后，