用新型神经网络提升航天器交会约束控制精度
CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
- 用柯尔莫戈洛夫-阿诺德网络构建约束感知控制器
- 在高偏心率轨道上实现更高精度的交会控制
- 适合航天控制与智能算法交叉研究者
本文针对航天器交会任务中的约束控制问题，提出一种基于约束感知神经网络（CINN）的时移调度器（TSG）近似方法。将新兴的人工智能架构——柯尔莫戈洛夫-阿诺德网络（KANs）作为核心组件，构建了约束感知柯尔莫戈洛夫-阿诺德网络（CIKAN），用于替代传统TSG。通过在高偏心率轨道上的约束交会仿真验证了CIKAN-TSG的有效性，并与基于多层感知机的CINN-TSG及传统TSG进行了对比。
原文摘要 · Abstract (English)
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.
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