面向物理模型的学习算法及其在无人机中的应用
A Preliminary Study on A Physical Model Oriented Learning Algorithm with Application to UAVs
摘要：
本文通过对第一性原理物理误差的推理，对一种有效的学习算法进行了初步研究，以近实时地产生学习信号。受迭代学习控制（ILC）的启发，将该学习算法应用于无人机的前馈控制回路中，使得能够从具有不同动态特性或在不同场景下飞行的其他无人机的误差中学习。该学习框架通过分析性地结合物理模型映射提高了数据利用效率和学习可靠性，并通过赋予其自学习能力增强了基于模型方法的灵活性。数值研究进行验证所提出的学习算法。
英文摘要：
This paper provides a preliminary study for an efficient learning algorithm by reasoning the error from first principle physics to generate learning signals in near real time. Motivated by iterative learning control (ILC), this learning algorithm is applied to the feedforward control loop of the unmanned aerial vehicles (UAVs), enabling the learning from errors made by other UAVs with different dynamics or flying in different scenarios. This learning framework improves the data utilization efficiency and learning reliability via analytically incorporating the physical model mapping, and enhances the flexibility of the model-based methodology with equipping it with the self-learning capability. Numerical studies are performed to validate the proposed learning algorithm.
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