Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141407
Title: Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge
Authors: Sarabakha, Andriy
Kayacan, Erdal
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Sarabakha, A., & Kayacan, E. (2019). Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge. Proceedings of 2019 International Conference on Robotics and Automation (ICRA). doi:10.1109/ICRA.2019.8794314
Abstract: 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.
URI: https://hdl.handle.net/10356/141407
ISBN: 978-1-5386-8176-3
DOI: 10.1109/ICRA.2019.8794314
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICRA.2019.8794314
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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