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Enhancing UAV Stability: A Deep Reinforcement Learning Strategy
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dc.contributor.authorKim, Junyoung-
dc.contributor.authorJung, Soyi-
dc.date.issued2024-01-01-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37127-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85189239523&origin=inward-
dc.description.abstractRecently, 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.-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.subject.meshAerial vehicle-
dc.subject.meshDeep deterministic policy gradient-
dc.subject.meshDeterministics-
dc.subject.meshInnovative approaches-
dc.subject.meshLearning strategy-
dc.subject.meshPolicy gradient-
dc.subject.meshQuad rotors-
dc.subject.meshReinforcement learnings-
dc.subject.meshUnmanned aerial vehicle-
dc.subject.meshVehicle stability-
dc.titleEnhancing UAV Stability: A Deep Reinforcement Learning Strategy-
dc.typeConference-
dc.citation.conferenceDate2024.1.28. ~ 2024.1.31.-
dc.citation.conferenceName2024 International Conference on Electronics, Information, and Communication, ICEIC 2024-
dc.citation.edition2024 International Conference on Electronics, Information, and Communication, ICEIC 2024-
dc.citation.title2024 International Conference on Electronics, Information, and Communication, ICEIC 2024-
dc.identifier.bibliographicCitation2024 International Conference on Electronics, Information, and Communication, ICEIC 2024-
dc.identifier.doi10.1109/iceic61013.2024.10457214-
dc.identifier.scopusid2-s2.0-85189239523-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10457047-
dc.subject.keywordAttitude Control-
dc.subject.keywordDDPG-
dc.subject.keywordQuadrotor-
dc.subject.keywordReinforcement Learning-
dc.subject.keywordUAV-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaComputer Networks and Communications-
dc.subject.subareaComputer Science Applications-
dc.subject.subareaHardware and Architecture-
dc.subject.subareaInformation Systems-
dc.subject.subareaEnergy Engineering and Power Technology-
dc.subject.subareaElectrical and Electronic Engineering-
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Department of Electrical and Computer Engineering
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