Deep Learning Based Neural Network Controller for Quad Copter: Application to Hovering Mode

Saleh Edhah, Somayya Mohamed, Ahmed Rehan, Mariam Aldhaheri, Adham Alkhaja, Yahya Zweiri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In the past few years, new advances in Deep Neural Networks (DNN) and Deep Learning (DL) has made it possible to control Rotary Unmanned Aerial Vehicles (RUAVs) with a variety of robust and intelligent techniques. In this work, a feedforward-based deep neural network is utilized to control the altitude, hovering mode, of an RUAV system. An automated search routine was developed to determine the optimum architecture of the neural network for the controller. This network was trained using the supervised learning technique, and the controller performance was compared for three different DL/DNN training paradigms; the standard feedforward method, the greedy layer-wise method, and the Long Short-Term Memory (LSTM) method in which the response of each controller was presented, where it was found that the greedy layer-wise method gives the most optimal result.

Original languageBritish English
Title of host publication2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155326
DOIs
StatePublished - Nov 2019
Event2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 - Ras Al Khaimah, United Arab Emirates
Duration: 19 Nov 201921 Nov 2019

Publication series

Name2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Conference

Conference2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period19/11/1921/11/19

Keywords

  • Deep Learning (DL)
  • Deep Neural Network (DNN)
  • LQR
  • Quadrotor Control
  • RUAV Control

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