Abstract

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.

Original languageEnglish
Title of host publication2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages165-169
Number of pages5
ISBN (Electronic)9781728173566
DOIs
Publication statusPublished - 24 Nov 2020
Event3rd International Conference on Information and Communications Technology, ICOIACT 2020 - Yogyakarta, Indonesia
Duration: 24 Nov 202025 Nov 2020

Publication series

Name2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020

Conference

Conference3rd International Conference on Information and Communications Technology, ICOIACT 2020
Country/TerritoryIndonesia
CityYogyakarta
Period24/11/2025/11/20

Keywords

  • UAV
  • deep learning
  • machine learning
  • quadcopter
  • system identification

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