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Erdal Kayacan

Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge

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DOI

  • Andriy Sarabakha, Nanyang Technological University
  • ,
  • Erdal Kayacan

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.

OriginalsprogEngelsk
Titel2019 International Conference on Robotics and Automation, ICRA 2019
Antal sider7
ForlagIEEE
Udgivelsesår2019
Sider7727-7733
Artikelnummer8794314
ISBN (Elektronisk)9781538660263
DOI
StatusUdgivet - 2019
Begivenhed2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Varighed: 20 maj 201924 maj 2019

Konference

Konference2019 International Conference on Robotics and Automation, ICRA 2019
LandCanada
ByMontreal
Periode20/05/201924/05/2019
SponsorBosch, DJI, et al., Kinova, Mercedes-Benz, Samsung

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