On low-complexity control design to spacecraft attitude stabilization: An online-learning approach
On low-complexity control design to spacecraft attitude stabilization: An online-learning approach
复制标题
航天器姿态稳定的低复杂度控制设计：一种在线学习方法
DOI：
10.1016/j.ast.2020.106441
复制
发表时间：
2021
影响因子：
5.6
通讯作者：
Li Bo
中科院分区：
文献类型：
--
作者：
Zhang Chengxi;Xiao Bing;Wu Jin;Li Bo
研究了具有外部扰动的航天器姿态稳定问题。提出了一种新的控制方案--基于线性学习控制的控制器，以实现鲁棒性、精确性和结构简单的控制算法。与传统的控制设计相比，在线学习控制算法的一个显著特点是它综合利用了前一次控制输入信息和系统当前状态信息，就像从前一次控制输入中学习经验一样。相比之下，传统的控制方案在生成控制指令时并不完全使用现有的信息，而是选择丢弃先前的控制输入信息。由于这种学习策略，在设计鲁棒控制律时，可以避免使用基于自适应或自适应的工具，从而使算法简单、有效，而且节省了系统资源。所设计的控制律能使姿态系统稳定，并具有一致最终有界收敛性。
This paper studies the spacecraft attitude stabilization problem with external disturbances. A new control scheme entitledonline-learning controlis proposed to achieve a robust, accurate, and simple-structure control algorithm. Compared with the conventional control design, an obvious distinction of the online-learning control algorithm is that it together utilizes the previous control input information and the system's current state information, as if learning experience from previous control input. In contrast, the conventional control scheme does not fully use the existing information and chooses to discard the previous control input information when generating control instructions. Due to the learning strategy, the utility of adaptive- or observer-based tools can be avoided when designing a robust control law, making a simple, effective algorithm, moreover saving system resources. The proposed control law can stabilize the attitude system by achieving the uniformly ultimately bounded convergence.