用神经网络预测飞行器负载扰动，提升控制精度与响应速度。
Neural Predictor for Flight Control with Payload
- 构建混合动力模型，融合物理规律与学习到的扰动动态。
- 实测误差降低66.15%（力）和33.33%（力矩），样本需求更少。
- 适合需高精度负载控制的无人机系统，如吊挂运输任务。
近年来，将悬吊负载作为自由浮动机械臂的空中机器人受到广泛关注。然而，负载引起的力/力矩及残余动态会引入未建模扰动，影响闭环控制性能。不同于估计算法，本文提出神经预测器（Neural Predictor），一种基于学习的方法，将负载与残余动态引发的力/力矩建模为动态系统，构建了结合第一性原理与学习动态的混合模型。该模型集成于模型预测控制（MPC）框架中，显著提升闭环性能。在数值仿真与真实飞行实验中均验证了有效性：所提方法能准确捕捉负载扰动，快速响应变化，且相比先进学习估计器，力与力矩估计误差分别降低66.15%与33.33%，同时所需训练样本更少。代码已开源：https://github.com/NPU-RCIR/Neural-Predictor.git。
原文摘要 · Abstract (English)
Aerial robotics for transporting suspended payloads as the form of freely-floating manipulator are growing great interest in recent years. However, the force/torque caused by payload and residual dynamics will introduce unmodeled perturbations to the aerial robotics, which negatively affects the closed-loop performance. Different from estimation-like methods, this paper proposes Neural Predictor, a learning-based approach to model force/torque induced by payload and residual dynamics as a dynamical system. It yields a hybrid model that combines the first-principles dynamics with the learned dynamics. The hybrid model is then integrated into a MPC framework to improve closed-loop performance. Effectiveness of proposed framework is verified extensively in both numerical simulations and real-world flight experiments. The results indicate that our approach can capture force/torque caused by suspended payload and residual dynamics accurately, respond quickly to the changes of them and improve the closed-loop performance significantly. In particular, Neural Predictor outperforms a state-of-the-art learning-based estimator and has reduced the force and torque estimation errors by up to 66.15% and 33.33% while requiring less samples. The code of proposed Neural Predictor can be found at https://github.com/NPU-RCIR/Neural-Predictor.git.
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