CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
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Abstract (arXiv)
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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References · 10
- 2024KAN: Kolmogorov-Arnold Networkscited by 649
- 2024Kolmogorov-Arnold Networks are Radial Basis Function Networkscited by 128
- 2024A Survey on Kolmogorov-Arnold Networkcited by 68
- 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 58
- 2019Large Scale Model Predictive Control with Neural Networks and Primal Active Setscited by 29
- 2024Kolmogorov-Arnold Network for Online Reinforcement Learningcited by 9
- 2022Neural Lyapunov Differentiable Predictive Controlcited by 7
- 2024HyperbolicLR: Epoch insensitive learning rate schedulercited by 3
- 2024KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problemscited by 3
- 2024Time Shift Governor for Constrained Control of Spacecraft Orbit and Attitude Relative Motion in Bicircular Restricted Four-Body Problemcited by 1
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