论文标题
使用集合学习的四四光操作估算精确的功耗估算的数据有效建模
Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning
论文作者
论文摘要
电动起飞和降落（EVTOL）飞机被认为是新兴的城市空气流动性中的主要飞机类型。准确的功耗估计对于EVTOL至关重要，支持先进的电力管理策略并提高飞行运营的效率和安全性能。在这项研究中，建立了EVTOL飞机功耗建模的框架。我们采用了一种合奏学习方法，即堆叠，使用三种不同类型的二次运动的飞行记录开发数据驱动的模型。选择了随机森林和极端梯度的增强，显示了预测的优势，被选为基本模型，并将线性回归模型用作元模型。已建立的堆叠模型可以准确估计四极管的功率。错误分析表明，大约80％的预测误差属于一个标准偏差间隔内，预计整个飞行的预测中的误差小于0.5％的误差，可以预期，信心超过80％。我们的模型在两个方面都优于现有模型：首先，我们的模型具有更好的预测性能，其次，我们的模型更具数据效率，需要较小的数据集。我们的模型为EVTOL飞机的运营商提供了一个强大的工具，并有助于促进安全和节能的城市空中交通。
Electric Take-Off and Landing (eVTOL) aircraft is considered as the major aircraft type in the emerging urban air mobility. Accurate power consumption estimation is crucial to eVTOL, supporting advanced power management strategies and improving the efficiency and safety performance of flight operations. In this study, a framework for power consumption modeling of eVTOL aircraft was established. We employed an ensemble learning method, namely stacking, to develop a data-driven model using flight records of three different types of quadrotors. Random forest and extreme gradient boosting, showing advantages in prediction, were chosen as base-models, and a linear regression model was used as the meta-model. The established stacking model can accurately estimate the power of a quadrotor. Error analysis shows that about 80% prediction errors fall within one standard deviation interval and less than 0.5% error in the prediction for an entire flight can be expected with a confidence of more than 80%. Our model outperforms the existing models in two aspects: firstly, our model has a better prediction performance, and secondly, our model is more data-efficient, requiring a much smaller dataset. Our model provides a powerful tool for operators of eVTOL aircraft in mission management and contributes to promoting safe and energy-efficient urban air traffic.
