通过物理信息归一化流学习鲁棒卫星姿态动力学
Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow
AI总结：
本文通过结合物理信息神经网络与归一化流模型，提升航天器姿态动力学控制的鲁棒性和稳定性，实验表明在MPC框架下，物理信息模型在控制精度和抗干扰能力上优于纯数据驱动方法。
摘要：
姿态控制是航天器操作中的基本方面。模型预测控制（MPC）已发展为一种强大的策略，依赖于系统动力学的准确模型来优化控制动作。在物理模型不完整、难以推导或计算成本高的情况下，机器学习提供了一种灵活的替代方法，通过直接从数据中学习系统行为。然而，纯数据驱动模型在泛化和稳定性方面往往存在困难，尤其是在应用于训练域之外的输入时。为了解决这些限制，我们研究了将物理信息神经网络（PINNs）融入航天器姿态动力学学习中的益处，将其性能与纯数据驱动方法进行比较。使用带有自注意力机制的实值非体积保持（Real NVP）神经网络架构，我们在Basilisk模拟器生成的模拟数据上训练了多个模型。考虑了两种训练策略：纯数据驱动基线和物理信息变体以提高鲁棒性和稳定性。我们的结果表明，加入物理信息显著提高了最佳架构的平均相对误差，提高了27.08%。这些优势在将学习模型集成到MPC框架中时尤为明显，其中PINN模型在控制精度和鲁棒性方面优于纯数据驱动方法，并在传统MPC方法中实现了更短的稳定时间，当受到观测噪声和RWs摩擦影响时，改进可达62%。
英文摘要：
Attitude control is a fundamental aspect of spacecraft operations. Model Predictive Control (MPC) has emerged as a powerful strategy for these tasks, relying on accurate models of the system dynamics to optimize control actions over a prediction horizon. In scenarios where physics models are incomplete, difficult to derive, or computationally expensive, machine learning offers a flexible alternative by learning the system behavior directly from data. However, purely data-driven models often struggle with generalization and stability, especially when applied to inputs outside their training domain. To address these limitations, we investigate the benefits of incorporating Physics-Informed Neural Networks (PINNs) into the learning of spacecraft attitude dynamics, comparing their performance with that of purely data-driven approaches. Using a Real-valued Non-Volume Preserving (Real NVP) neural network architecture with a self-attention mechanism, we trained several models on simulated data generated with the Basilisk simulator. Two training strategies were considered: a purely data-driven baseline and a physics-informed variant to improve robustness and stability. Our results demonstrate that the inclusion of physics-based information significantly enhances the performance in terms of the mean relative error with the best architectures found by 27.08%. These advantages are particularly evident when the learned models are integrated into an MPC framework, where PINN-based models consistently outperform their purely data-driven counterparts in terms of control accuracy and robustness, and achieve improved settling times when compared to traditional MPC approaches, yielding improvements of up to 62%, when subject to observation noise and RWs friction.
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