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

TL;DR

This paper introduces a hybrid control approach combining physics-informed neural networks with model predictive control for satellite attitude management, significantly enhancing prediction accuracy and response speed under uncertainties.

Contribution

It demonstrates the effectiveness of physics-informed neural networks in modeling spacecraft dynamics and integrating them into MPC for improved control performance.

Findings

Physics-informed models reduce prediction error by over 68%.

Hybrid control achieves 61-76% faster settling times under noise.

Enhanced robustness and steady-state convergence in satellite attitude control.

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.