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Learning-Based Adaptive Attitude Control of Spacecraft Formation With Guaranteed Prescribed Performance
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2019-11-01 , DOI: 10.1109/tcyb.2018.2857400
Caisheng Wei , Jianjun Luo , Honghua Dai , Guangren Duan

This paper investigates a novel leader-following attitude control approach for spacecraft formation under the preassigned two-layer performance with consideration of unknown inertial parameters, external disturbance torque, and unmodeled uncertainty. First, two-layer prescribed performance is preselected for both the attitude angular and angular velocity tracking errors. Subsequently, a distributed two-layer performance controller is devised, which can guarantee that all the involved closed-loop signals are uniformly ultimately bounded. In order to tackle the defect of statically two-layer performance controller, learning-based control strategy is introduced to serve as an adaptive supplementary controller based on adaptive dynamic programming technique. This enhances the adaptiveness of the statically two-layer performance controller with respect to unexpected uncertainty dramatically, without any prior knowledge of the inertial information. Furthermore, by employing the robustly positively invariant theory, the input-to-state stability is rigorously proven under the designed learning-based distributed controller. Finally, two groups of simulation examples are organized to validate the feasibility and effectiveness of the proposed distributed control approach.

中文翻译:

保证规定性能的基于学习的航天器编队自适应姿态控制

本文研究了在未知的惯性参数,外部扰动转矩和未建模的不确定性的情况下,在预先指定的两层性能下用于航天器编队的新型跟随跟随姿态控制方法。首先,针对姿态角和角速度跟踪误差,预先选择了两层规定的性能。随后,设计了一个分布式的两层性能控制器,该控制器可以确保所有相关的闭环信号最终均匀地有界。为了解决静态两层性能控制器的缺陷,引入了基于学习的控制策略作为基于自适应动态规划技术的自适应辅助控制器。在没有惯性信息的任何先验知识的情况下,这极大地增强了静态两层性能控制器针对意外不确定性的适应性。此外,通过采用鲁棒的正不变理论,在设计的基于学习的分布式控制器下,输入到状态的稳定性得到了严格的证明。最后,组织了两组仿真示例,以验证所提出的分布式控制方法的可行性和有效性。
更新日期:2019-11-01
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