UAMDynCon-DT: A Data-driven Dynamics and Robust Control Framework for UAM Vehicle Digitalization using Deep Learning-Reference-Cited by-同舟云学术

UAMDynCon-DT: A Data-driven Dynamics and Robust Control Framework for UAM Vehicle Digitalization using Deep Learning

Published:2023-02-18 Issue: Volume: Page:
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Container-title:2023 International Conference on Mechatronics, Control and Robotics (ICMCR)
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Author:

Jang Minseok¹,Hyun Jeongseok¹,Kwag Taeho¹,Gwak Chan¹,Nguyen Tuan Anh²,Lee Jae-Woo²

Affiliation:

1. Konkuk University,Department of Aerospace Engineering,Seoul,South Korea

2. Konkuk Aerospace Design-Airworthiness Research Institute (KADA) Konkuk University,Seoul,South Korea

Funder

National Research Foundation of Korea

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10180903/10181028/10181072.pdf?arnumber=10181072

Reference9 articles.

1. Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving

2. Neural dynamics for multicopter digital twin using deep learning;jang;2022 The Korean Society for Aeronautical and Space Sciences Fall Conference,2022

3. Set-Theoretic Methods in Control

4. Attitude stabilization of evtol in hovering mode using neural network based control with lyapunov theorem;jang;2022 The Korean Society for Aeronautical and Space Sciences Spring Conference,2022

5. Learning‐based robust neuro‐control: A method to compute control Lyapunov functions

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