A Deep Learning Neural Network Control Approach for Quadrotor UAV Landing on a Moving Platform

Jiahan Peng, Kewei Xia*

*Corresponding author for this work

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

Abstract

A deep learning control approach for autonomous landing of the quadrotor unmanned aerial vehicle (UAV) is investigated. First, the error position dynamics concerning the moving target and the error attitude dynamics are described. Then, the force and torque controllers are developed, where the deep learning neural network (DNN) compositing of output-layers, input-layers and modular neural functions is utilized to counteract the system uncertainty. Stability analysis demonstrates that the closed-loop systems are uniformly ultimately bounded. Finally, the proposed strategy is validated through simulation examples.

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 1
EditorsLiang Yan, Haibin Duan, Yimin Deng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages486-495
Number of pages10
ISBN (Print)9789819621996
DOIs
Publication statusPublished - 2025
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, China
Duration: 9 Aug 202411 Aug 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1337 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2024
Country/TerritoryChina
CityChangsha
Period9/08/2411/08/24

Keywords

  • Autonomous Landing
  • Deep Learning Neural Network (DNN)
  • Unmanned Aerial Vehicle (UAV)

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