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

2026-02-17

AI Summary

This work proposes a hybrid control framework that integrates physics-informed neural networks (PINNs) with model predictive control (MPC) to address the limitations of both traditional and purely data-driven approaches for satellite attitude control. Traditional methods rely on high-fidelity dynamical models that are complex and computationally expensive, whereas data-driven strategies often suffer from poor generalization and inadequate stability guarantees. By embedding physical priors into neural network training, the proposed approach constructs a robust and accurate surrogate model of attitude dynamics, which is then combined with a linear nominal model to form a nonlinear–linear hybrid MPC architecture. Experimental results demonstrate that, compared to purely data-driven methods, the proposed framework reduces the mean relative prediction error by 68.17% and shortens closed-loop settling time by 61.52%–76.42% under measurement noise and reaction wheel friction disturbances, while ensuring stable convergence.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• satellite attitude control
• model predictive control
• physics-informed neural networks
• system identification
• dynamics modeling

Innovation

• Physics-Informed Neural Networks
• Model Predictive Control
• Hybrid Control
• Spacecraft Attitude Dynamics
• Data-Driven Modeling