TY - GEN
T1 - A Dynamic Evolution Method for Digital Twins Based on RDD-RNN
AU - Cheng, Hongbo
AU - Zhang, Lin
AU - Wang, Kunyu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Digital twin technology, as a forefront of research, aims to seamlessly merge physical objects with virtual models, providing new perspectives and solutions to tackle interdisciplinary challenges. Presently, research on algorithms for digital twin models has achieved the accurate prediction of system behavior and performance. Nonetheless, digital twin technology still confronts challenges, such as intricate system modeling and real-time demands. In response, this paper proposes a Real-time Data-Driven Recurrent Neural Network (RDD-RNN) model dynamic evolution approach. By employing data-driven approaches to comprehend the inherent relationships of mechanistic models, the reliance of the twin model on these mechanisms is lessened. Additionally, real-time data is assimilated into the model network in real-time, achieving the dynamic evolution of the twin model. Finally, the effectiveness of the RDD-RNN method was verified through the establishment of a digital twin model for unmanned aerial vehicles and the validation of data related to unmanned equipment flight attitudes control.
AB - Digital twin technology, as a forefront of research, aims to seamlessly merge physical objects with virtual models, providing new perspectives and solutions to tackle interdisciplinary challenges. Presently, research on algorithms for digital twin models has achieved the accurate prediction of system behavior and performance. Nonetheless, digital twin technology still confronts challenges, such as intricate system modeling and real-time demands. In response, this paper proposes a Real-time Data-Driven Recurrent Neural Network (RDD-RNN) model dynamic evolution approach. By employing data-driven approaches to comprehend the inherent relationships of mechanistic models, the reliance of the twin model on these mechanisms is lessened. Additionally, real-time data is assimilated into the model network in real-time, achieving the dynamic evolution of the twin model. Finally, the effectiveness of the RDD-RNN method was verified through the establishment of a digital twin model for unmanned aerial vehicles and the validation of data related to unmanned equipment flight attitudes control.
KW - data-driven
KW - digital twin
KW - dynamic evolution
KW - recurrent neural network
UR - https://www.scopus.com/pages/publications/85179012589
U2 - 10.1109/CCCI58712.2023.10290812
DO - 10.1109/CCCI58712.2023.10290812
M3 - 会议稿件
AN - SCOPUS:85179012589
T3 - Proceedings of the 2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023
BT - Proceedings of the 2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023
A2 - Obaidat, Mohammad S.
A2 - Ning, Zhaolong
A2 - Hsiao, Kuei-Fang
A2 - Nicopolitidis, Petros
A2 - Guo, Yu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023
Y2 - 18 October 2023 through 20 October 2023
ER -