In practical flight process, the time-varying disturbances are often encountered by the space unmanned systems, and the attitude outputs of the space unmanned systems are required to stay in the predefined intervals. Moreover, the convergence process of the space unmanned systems has to be achieved in finite time, to complete the given tasks. However, most of the existing results are difficult to be applied to give consideration to the finite-time-convergence, the constrained outputs and the suppression for the time-varying disturbances simultaneously. To address this problem, in this article, we propose a novel fuzzy adaptive finite-time anti-disturbance control scheme for the space unmanned systems. The super-twisting disturbance observer, which possess the robust and finite-time disturbance estimation ability, has been utilized to suppress the time-varying disturbances. The nonlinear signal transformation technique has been introduced, transforming the constrained outputs into a novel unconstrained auxiliary variable, and the output constrained control issue becomes an equivalent bounded control problem. To achieve the finite-time convenience of the closed-loop space unmanned system, the fractional control laws have been designed, and an important lemma has been utilized to design the update laws of the adaptive parameters. Moreover, the fuzzy logic systems have been used to improve the robustness respect to the uncertainties. The contrastive simulation results have been provided, the finite-time control ability of the proposed method and the satisfactory estimation performance of the super-twisting disturbance observer can be observed.

在实际的飞行过程中，空间无人系统经常遇到时变扰动，要求空间无人系统的姿态输出保持在预定的间隔内。而且，必须在有限的时间内完成空间无人系统的收敛过程，以完成给定的任务。但是，现有的大多数结果很难同时考虑有限时收敛，受约束的输出和时变扰动的抑制。为了解决这个问题，在本文中，我们为空间无人系统提出了一种新颖的模糊自适应有限时防扰动控制方案。超扭转扰动观测器，具有鲁棒且有限时间的扰动估计能力，已被用来抑制随时间变化的干扰。引入了非线性信号变换技术，将约束输出变换为新颖的无约束辅助变量，输出约束控制问题成为等效的有界控制问题。为了实现闭环空间无人系统的有限时间便利性，设计了分数控制律，并利用重要引理设计了自适应参数的更新律。此外，模糊逻辑系统已被用于提高不确定性方面的鲁棒性。提供了对比仿真结果，所提出方法的有限时间控制能力和超扭转扰动观测器的令人满意的估计性能。