CIKAN：面向自主航天器交会的、采用时间移位调节器的约束感知Kolmogorov-Arnold网络
CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
AI总结：
该研究将新兴KAN架构引入约束感知神经网络，提出基于CIKAN的时间移位调节器近似方法，用于航天器交会的约束施加，仿真验证了其在高椭圆轨道交会任务中的有效性，并对比了其与MLP基CINN、传统TSG的性能。
摘要：
本文研究了时间移位调节器（TSG）的约束感知神经网络（CINN）近似方法——TSG是标称闭环系统的附加方案，在航天器交会应用中通过时移参考轨迹来施加约束。我们引入AI领域新兴架构Kolmogorov-Arnold网络（KANs）作为CINN的核心组件，提出了基于约束感知Kolmogorov-Arnold网络（CIKAN）的TSG近似方法。我们通过高椭圆轨道上的约束航天器交会任务仿真验证了基于CIKAN的TSG的有效性，并对比了CIKAN、基于MLP的CINN与传统TSG的性能。
英文摘要：
The paper considers a Constrained-Informed Neural Network (CINN) approximation for the Time Shift Governor (TSG), which is an add-on scheme to the nominal closed-loop system used to enforce constraints by time-shifting the reference trajectory in spacecraft rendezvous applications. We incorporate Kolmogorov-Arnold Networks (KANs), an emerging architecture in the AI community, as a fundamental component of CINN and propose a Constrained-Informed Kolmogorov-Arnold Network (CIKAN)-based approximation for TSG. We demonstrate the effectiveness of the CIKAN-based TSG through simulations of constrained spacecraft rendezvous missions on highly elliptic orbits and present comparisons between CIKANs, MLP-based CINNs, and the conventional TSG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
