This article addresses an attitude stabilization control problem for the asymmetric underactuated tethered spacecraft subject to external disturbances, and a reinforcement learning(RL)-based finite time control scheme is proposed to enhance the control performance and energy efficiency of the closed-loop system. Firstly, the error dynamics of the underactuated tethered system in the presence of external disturbances is built based on the Lagrange’s modeling technique. Then, a RL-based control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor–critic networks are developed to obtain the optimal performance index function and the optimal controller. According to the Lyapunov theorem, semi-global finite-time stability of all the closed-loop signals is achieved through rigorous mathematical analysis, and tracking errors can be ensured to an arbitrarily small neighborhood of the origin in a finite time. Finally, comparative simulation results with hierarchical sliding mode controller are presented to demonstrate the viability of the proposed strategy.

中文翻译：

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基于强化学习的非对称欠驱动扰动系留航天器有限时间控制

针对受到外部干扰的非对称欠驱动系留航天器的姿态稳定控制问题，提出了一种基于强化学习（RL）的有限时间控制方案，以提高闭环系统的控制性能和能量效率。首先，基于拉格朗日建模技术，建立了存在外部干扰时欠驱动系留系统的误差动力学。然后，通过径向基函数（RBF）神经网络（NN）实现基于强化学习的控制算法，其中开发行动者-批评者网络以获得最佳性能指标函数和最佳控制器。根据李亚普诺夫定理，通过严格的数学分析，实现了所有闭环信号的半全局有限时间稳定性，并且可以保证在有限时间内跟踪误差达到原点的任意小邻域。最后，给出了与分层滑模控制器的比较仿真结果，以证明所提出策略的可行性。