CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
This is an incremental improvement for spacecraft rendezvous applications, addressing constraint enforcement in autonomous systems.
The paper tackled the problem of enforcing constraints in autonomous spacecraft rendezvous by proposing a Constraint-Informed Kolmogorov-Arnold Network (CIKAN) approximation for the Time Shift Governor, demonstrating its effectiveness through simulations on highly elliptic orbits with comparisons to MLP-based methods and conventional TSG.
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.