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

TY - GEN

T1 - Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge

AU - Sarabakha, Andriy

AU - Kayacan, Erdal

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85071439874&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2019.8794314

DO - 10.1109/ICRA.2019.8794314

M3 - Article in proceedings

AN - SCOPUS:85071439874

SP - 7727

EP - 7733

BT - 2019 International Conference on Robotics and Automation, ICRA 2019

PB - IEEE

T2 - 2019 International Conference on Robotics and Automation, ICRA 2019

Y2 - 20 May 2019 through 24 May 2019

ER -