Data-efficient modeling for power consumption estimation of quadrotor operations using ensemble learning

• Main Authors: Dai, Wei, Zhang, Mingcheng, Low, Kin Huat
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Aeronautical engineering::Aviation; Intelligent Transportation System; Urban Air Mobility
• Online Access: https://hdl.handle.net/10356/172654
• Institution: Nanyang Technological University

Electric Take-Off and Landing (eVTOL) aircraft is considered the major aircraft type in 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 quadrotor drones, as a representative 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 regressor 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.