TY - GEN
T1 - Deep Learning based System Identification of Quadcopter Unmanned Aerial Vehicle
AU - Amiruddin, Brilian Putra
AU - Iskandar, Eka
AU - Fatoni, Ali
AU - Santoso, Ari
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - Acquiring the systems' model is a significant stride of controller design and development. Nevertheless, it is arduous sometimes to estimate a real-world system model such as a quadcopter UAV, which has non-linearity, difficult-to-measure, and complex characteristics. This condition caused system identification plays a crucial role in the quadcopter modeling. With the ease of obtaining the experimental flight data directly from the quadcopter, hence modeling of the quadcopter using system identification now is handy. Conforming to that, the development of machine learning algorithmsspecifically on the deep learning field, driven new perspectives on the system identification approach. In this paper, several deep learning architectures were applied to identify the quadcopter UAVsystem. Overall, results show that the CNN-LSTM model was the top-performed architecture with the average tested MSE and MAE equal to 0.0002 and 0.0030.
AB - Acquiring the systems' model is a significant stride of controller design and development. Nevertheless, it is arduous sometimes to estimate a real-world system model such as a quadcopter UAV, which has non-linearity, difficult-to-measure, and complex characteristics. This condition caused system identification plays a crucial role in the quadcopter modeling. With the ease of obtaining the experimental flight data directly from the quadcopter, hence modeling of the quadcopter using system identification now is handy. Conforming to that, the development of machine learning algorithmsspecifically on the deep learning field, driven new perspectives on the system identification approach. In this paper, several deep learning architectures were applied to identify the quadcopter UAVsystem. Overall, results show that the CNN-LSTM model was the top-performed architecture with the average tested MSE and MAE equal to 0.0002 and 0.0030.
KW - UAV
KW - deep learning
KW - machine learning
KW - quadcopter
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=85100887703&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT50329.2020.9332059
DO - 10.1109/ICOIACT50329.2020.9332059
M3 - Conference contribution
AN - SCOPUS:85100887703
T3 - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
SP - 165
EP - 169
BT - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Communications Technology, ICOIACT 2020
Y2 - 24 November 2020 through 25 November 2020
ER -