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Enhancing UAV Stability: A Deep Reinforcement Learning Strategy
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Publication Year
2024-01-01
Journal
2024 International Conference on Electronics, Information, and Communication, ICEIC 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2024 International Conference on Electronics, Information, and Communication, ICEIC 2024
Keyword
Attitude ControlDDPGQuadrotorReinforcement LearningUAV
Mesh Keyword
Aerial vehicleDeep deterministic policy gradientDeterministicsInnovative approachesLearning strategyPolicy gradientQuad rotorsReinforcement learningsUnmanned aerial vehicleVehicle stability
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsComputer Science ApplicationsHardware and ArchitectureInformation SystemsEnergy Engineering and Power TechnologyElectrical and Electronic Engineering
Abstract
Recently, as the utilization of unmanned aerial vehicles (UAVs) extends into increasingly varied domains, the control of UAV flight dynamics has become crucial. This paper presents an innovative approach for controlling UAV flight dynamics based on the deep deterministic policy gradient (DDPG) algorithm within the reinforcement learning (RL) paradigm. The proposed method is designed to rapidly adapt and maintain stable flight attitudes, outperforming conventional proportional integral derivative (PID) control methods. Utilizing a quadcopter UAV model equipped with four motors as the basis, this paper presents a strategy for UAV flight dynamics control. This strategy demonstrates flexibility and efficiency in a variety of scenarios and is scalable to accommodate UAVs of larger dimensions.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37127
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85189239523&origin=inward
DOI
https://doi.org/10.1109/iceic61013.2024.10457214
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10457047
Type
Conference
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Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
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