CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous Using Time Shift Governor
Abstract
This paper considers a Constraints-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 Constraints-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.
Cite
Text
Kim et al. "CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous Using Time Shift Governor." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.Markdown
[Kim et al. "CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous Using Time Shift Governor." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.](https://mlanthology.org/l4dc/2025/kim2025l4dc-cikan/)BibTeX
@inproceedings{kim2025l4dc-cikan,
title = {{CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous Using Time Shift Governor}},
author = {Kim, Taehyeun and Girard, Anouck and Kolmanovsky, Ilya},
booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference},
year = {2025},
pages = {1115-1126},
volume = {283},
url = {https://mlanthology.org/l4dc/2025/kim2025l4dc-cikan/}
}