Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control

• URL: http://arxiv.org/abs/2602.15954v1
• Date: Tue, 17 Feb 2026 19:08:48 GMT
• Title: Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control
• Authors: Carlo Cena, Mauro Martini, Marcello Chiaberge,
• Abstract summary: Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics.<n>For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy.<n>This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics.
• Score: 2.7222301668137483
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics, particularly when model-based strategies such as Model Predictive Control (MPC) are employed, where performance is limited by the quality of the internal system model. For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy. Learning-based system identification presents a compelling alternative; however, models trained exclusively on data frequently exhibit fragile stability properties and limited extrapolation capability. This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics and contrasts it with a conventional data-driven approach. A comprehensive dataset is generated using high-fidelity numerical simulations, and two learning methodologies are investigated: a purely data-driven pipeline and a physics-regularized approach that incorporates prior knowledge into the optimization process. The results indicate that embedding physical constraints during training leads to substantial improvements in predictive reliability, achieving a 68.17% decrease in mean relative error relative. When deployed within an MPC architecture, the physics-informed models yield superior closed-loop tracking performance and improved robustness to uncertainty. Furthermore, a hybrid control formulation that merges the learned nonlinear dynamics with a nominal linear model enables consistent steady-state convergence and significantly faster response, reducing settling times by 61.52%-76.42% under measurement noise and reaction wheel friction.

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