标题： PI-WAN：一种物理信息风适应网络，用于未知环境中的四旋翼动力学预测 显示英文标题

标题： PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments

摘要： 精确的动力学建模对于四旋翼飞行器在各种应用中实现精确的轨迹跟踪至关重要。 传统的基于物理知识驱动的建模方法在具有可变载荷、风扰和外部扰动的未知环境中面临重大限制。 另一方面，数据驱动的建模方法在处理分布外（OoD）数据时泛化能力较差，限制了其在未知场景中的有效性。 为了解决这些挑战，我们引入了物理感知风适应网络（PI-WAN），通过将物理约束直接嵌入训练过程，结合知识驱动和数据驱动的建模方法，以实现鲁棒的四旋翼动力学学习。 具体而言，PI-WAN采用了一种时间卷积网络（TCN）架构，能够从历史飞行数据中高效捕捉时间依赖性，同时物理感知损失函数应用物理原理以提高模型在之前未见过条件下的泛化能力和鲁棒性。 通过将实时预测结果纳入模型预测控制（MPC）框架，我们实现了闭环跟踪性能的提升。 全面的仿真和真实飞行实验表明，我们的方法在预测精度、跟踪精度和对未知环境的鲁棒性方面优于基线方法。 显示英文摘要

摘要： Accurate dynamics modeling is essential for quadrotors to achieve precise trajectory tracking in various applications. Traditional physical knowledge-driven modeling methods face substantial limitations in unknown environments characterized by variable payloads, wind disturbances, and external perturbations. On the other hand, data-driven modeling methods suffer from poor generalization when handling out-of-distribution (OoD) data, restricting their effectiveness in unknown scenarios. To address these challenges, we introduce the Physics-Informed Wind-Adaptive Network (PI-WAN), which combines knowledge-driven and data-driven modeling methods by embedding physical constraints directly into the training process for robust quadrotor dynamics learning. Specifically, PI-WAN employs a Temporal Convolutional Network (TCN) architecture that efficiently captures temporal dependencies from historical flight data, while a physics-informed loss function applies physical principles to improve model generalization and robustness across previously unseen conditions. By incorporating real-time prediction results into a model predictive control (MPC) framework, we achieve improvements in closed-loop tracking performance. Comprehensive simulations and real-world flight experiments demonstrate that our approach outperforms baseline methods in terms of prediction accuracy, tracking precision, and robustness to unknown environments.