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On low-complexity control design to spacecraft attitude stabilization: An online-learning approach
Aerospace Science and Technology ( IF 5.6 ) Pub Date : 2020-12-30 , DOI: 10.1016/j.ast.2020.106441
Chengxi Zhang , Bing Xiao , Jin Wu , Bo Li

This paper studies the spacecraft attitude stabilization problem with external disturbances. A new control scheme entitled online-learning control is 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.



中文翻译:

关于低复杂度控制设计到航天器姿态稳定的在线学习方法

本文研究了具有外部干扰的航天器姿态稳定问题。一种名为在线学习控制的新控制方案提出了一种实现鲁棒,准确,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明显区别是它结合了先前的控制输入信息和系统的当前状态信息,就像从先前的控制输入中学习经验一样。相反,常规控制方案没有充分利用现有信息,而是在生成控制指令时选择丢弃先前的控制输入信息。由于采用了学习策略,因此在设计鲁棒的控制律时可以避免使用基于自适应工具或基于观察者的工具,从而简化了算法,并有效地节省了系统资源。

更新日期:2021-01-07
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