Jun 2026· arXiv.org· Vol abs/2606.29825· 0 citations· 33 references
Computer Science
TL;DR
This work proposes the Koopman Graph Dynamics 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 and develops a KGD based Model Predictive Control strategy for tethered space systems.
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.
A physics-informed Koopman representation based on generalized momenta is introduced, yielding a linear control-affine model in lifted coordinates with known input structure that avoids the bilinear state – input coupling inherent in standard Koopman approaches, enabling improved prediction accuracy and tractable controller synthesis.
The weak graph Koopman bilinear form model is proposed, which integrates geometric deep learning and Koopman theory to learn latent-space dynamics for networked systems, especially for challenging cases that have multiple timescales.
Yin Yu, Daning Huang, Seho Park et al.· Journal of Guidance Control...· 0 citations
This paper introduces Hybrid Physics-Augmented Neural Network (HyPA-Net), a hybrid modeling framework that integrates physics-based linear time-invariant models with artificial neural networks (ANNs) to address dynamic system modeling when only partial physical knowledge is available. The approach leverages the interpretability and robustness of established physical models while using ANNs—such as long short-term memory architectures—to capture unknown or nonlinear system behaviors. The methodology normalizes state and input variables for compatibility with ANN training and expands traditional recursive state-space equations for efficient backpropagation over sequences. Vehicle dynamics, specifically using a rear-wheel steering test case, validate the proposed framework. Various HyPA-Net configurations are benchmarked against pure physics-based and pure data-driven models, demonstrating improved prediction accuracy and model flexibility. The experimental results in this application confirm that hybrid models yield superior performance over strict physical approaches and can implicitly approximate submodel dynamics within a unified, yet modular, architecture, opening avenues for applications in domains where partial physics-based knowledge is available but insufficient on its own.
Laurin Ludmann, Jaeyoun Choi, J. Neubeck et al.· Vehicles· 0 citations
Real-time control of multivariable nonlinear processes requires models balancing high fidelity with computational tractability. This paper compares two data-driven paradigms: Bilinear Koopman Realizations and Physics-Informed Neural Networks. While standard Koopman approaches seek global linearization, we leverage a bilinear framework in the lifted functional space to preserve the natural coupling of control-affine systems. Simultaneously, PINNs ensure physical consistency by embedding conservation laws into the learning objective. To facilitate high-performance control, both surrogate models are integrated into a nonlinear model predictive control scheme using the CasADi framework, enabling efficient algorithmic differentiation for optimization. Simulation results for a quadruple tank system demonstrate that both paradigms reach mean VAF values above 99%, but the Bilinear Koopman model delivers a lower mean RMSE during step transients while doubling the computational speed of the PINN with a Real-Time Factor above 22. We conclude that despite structural scaling limitations regarding neural exploding gradients and operator instability risks, the Bilinear Koopman realization provides a more reliable, noise-resilient solution for real-time hydraulic benchmarks.
Amir Vanegas, Julio Barón-Velandia, Nelson Leonardo Díaz-Aldana et al.· International Conference on...· 0 citations
Accurate prediction of vehicle dynamics is critical for reliable motion planning and control of autonomous vehicles. However, physics-based models often struggle to represent the strong nonlinearity and coupling inherent in vehicle dynamics, while purely data-driven models frequently deteriorate when operating conditions deviate from the training domain. Physics-informed neural networks (PINNs) alleviate this issue by incorporating physical priors, but their performance can still be constrained by incomplete physical equations. To address these limitations, this paper proposes a Decoupled Hybrid Residual Model (DHRM) for online adaptive prediction of vehicle dynamics. The overall modeling residual is decomposed into a static structural component and a dynamic stochastic component, which are compensated through an offline channel and an online channel, respectively. The offline static channel combines Kolmogorov-Arnold networks (KAN) and long short-term memory (LSTM) networks to learn the static residual arising from structural simplification and parameter uncertainty. Concurrently, the online dynamic channel employs a sparse Gaussian process (SGP) to adaptively compensate the dynamic residual induced by changing operating conditions and external disturbances. Extensive simulation and scaled vehicle experiments validate the proposed framework. Under an abrupt friction change, DHRM-SGP reduces the root mean square error (RMSE) of lateral velocity and yaw rate by 67.5% and 70.5%, respectively, compared with the nominal physical model. The deployed model achieves an average inference latency of
4
.
6036
ms
per sample on the Jetson Orin NX, supporting the feasibility of deploying the prediction module on onboard hardware. These results demonstrate that the proposed architecture effectively improves prediction accuracy, robustness, and implementation feasibility under varying driving conditions.
Guodong Zhu, Jialing Yao, Yiwen Bai et al.· Proceedings of the Instituti...· 0 citations
This work uses SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings, alleviating the curse of dimensionality.
Matteo Tomasetto, Francesco Braghin, J. Kutz et al.· 0 citations