@inproceedings{2a7a1be20c6c4973b7d53073b6e3e374,
title = "A New Integrated Health Management for Quadrotors Based on Deep Learning",
abstract = "In this paper, the fault diagnosis and health monitoring techniques based on machine learning are investigated before the research. Then, the dynamic model of the quadrotor is analyzed and the most important relationship between the IMU and motor outputs are given. Afterwards, the high-coupling and nonlinear link between units are mapped into an implicit network APN based on LSTM neural network. Predictions and train are made based on the data set collected from the practical and HITL flight log. Finally, the feasibility of the health management is verified during the fault simulation and the a possible solution is given against the common error.",
keywords = "Dynamic model, Fault diagnosis, Health management, LSTM, Quadrotor",
author = "Weiyi Kong and Shaobo Bian and Xiaoyan Li and Chunyan Wang and Jianan Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; Conference date: 14-05-2021 Through 16-05-2021",
year = "2021",
month = may,
day = "14",
doi = "10.1109/DDCLS52934.2021.9455622",
language = "English",
series = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1418--1423",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
address = "United States",
}