Data-Driven Modeling and Control for Tethered Space Systems with Koopman-Informed Graphs
2026-06-29 • Robotics
Robotics
AI summaryⓘ
The authors address the challenge of modeling and controlling flexible space systems like tethers and nets, which have complex movements. They introduce a new approach called Koopman Graph Dynamics (KGD) that combines a mathematical tool (Koopman operator) with graph-based learning to better predict these systems' behavior. Their method works well in experiments and can apply to bigger systems than initially trained on, without needing to relearn. They also test their approach in orbit simulations, showing it can effectively control tethered space systems.
Tethered space systemsFlexible dynamicsKoopman operatorGraph Neural NetworksModel Predictive ControlData-driven modelingSpatial generalizationNonlinear dynamicsOrbital operations
Authors
Ao Jin, Yifeng Ma, Panfeng Huang, Fan Zhang
Abstract
Modeling tethered space systems is critical for advanced orbital operations. Flexible components such as tethers and space nets are integral to these systems but present significant control challenges due to their high dimensional, strongly coupled, and nonlinear dynamics. While data driven methods offer alternative modeling approaches, they frequently struggle with long term predictive stability and spatial generalization. To address this, we propose the Koopman Graph Dynamics (KGD) framework to learn the structural dynamics by integrating the global linear evolution of the Koopman operator with the local topological priors of Graph Neural Networks. Building upon this representation, we develop a KGD based Model Predictive Control strategy for tethered space systems. Subsequently, the ground experiments on flexible tether and space net demonstrate the high precision modeling capabilities of the proposed method. Crucially, the framework exhibits exceptional capacity for spatial transfer without retraining. Models trained exclusively on small configurations successfully predict and control significantly larger, unseen physical scales. Furthermore, the orbit simulations within a physics engine verify the effectiveness of the proposed approach for tethered space systems.