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.

翻译：可靠的航天器姿态控制依赖于对姿态动力学的精确预测，尤其当采用基于模型的策略（如模型预测控制（MPC））时，其性能受限于内部系统模型的质量。对于具有复杂动力学的航天器，获取精确的基于物理的模型可能十分困难、耗时或计算量大。基于学习的系统辨识提供了一种引人注目的替代方案；然而，完全基于数据训练的模型通常表现出脆弱的稳定性与有限的外推能力。本研究探索了使用物理信息神经网络（PINN）对航天器姿态动力学进行建模，并将其与常规数据驱动方法进行了对比。通过高保真数值仿真生成了一个综合数据集，并研究了两种学习方法：纯数据驱动流程和一种将先验知识融入优化过程的物理正则化方法。结果表明，在训练过程中嵌入物理约束能显著提升预测可靠性，平均相对误差降低了68.17%。当在MPC架构中部署时，物理信息模型展现出更优的闭环跟踪性能以及对不确定性的更强鲁棒性。此外，一种将学习到的非线性动力学与标称线性模型相结合的混合控制框架，能够实现一致的稳态收敛并显著加快响应速度，在测量噪声和反作用轮摩擦条件下，调节时间减少了61.52%至76.42%。