Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network SCIE SCOPUS

DC Field Value Language
dc.contributor.author Jeong, Sang Ki -
dc.contributor.author Ji, Dae Hyeong -
dc.contributor.author Oh, Ji Youn -
dc.contributor.author Seo, Jungmin -
dc.contributor.author Choi, Hyeung-sik -
dc.date.accessioned 2021-01-20T08:14:23Z -
dc.date.available 2021-01-20T08:14:23Z -
dc.date.created 2021-01-08 -
dc.date.issued 2021-04 -
dc.identifier.issn 1064-1246 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/39535 -
dc.description.abstract In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship's movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9]. -
dc.description.uri 1 -
dc.language English -
dc.publisher IOS PRESS -
dc.title Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network -
dc.type Article -
dc.citation.endPage 8011 -
dc.citation.startPage 8001 -
dc.citation.title JOURNAL OF INTELLIGENT & FUZZY SYSTEMS -
dc.citation.volume 40 -
dc.citation.number 4 -
dc.contributor.alternativeName 정상기 -
dc.contributor.alternativeName 지대형 -
dc.contributor.alternativeName 오지윤 -
dc.contributor.alternativeName 서정민 -
dc.identifier.bibliographicCitation JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.40, no.4, pp.8001 - 8011 -
dc.identifier.doi 10.3233/JIFS-189622 -
dc.identifier.scopusid 2-s2.0-85104316318 -
dc.identifier.wosid 000640545600022 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.description.isOpenAccess N -
dc.subject.keywordAuthor USV (Unmanned surface vehicles) -
dc.subject.keywordAuthor RNN (Recurrent neural network) -
dc.subject.keywordAuthor LSTM (Long short-term memory models) -
dc.subject.keywordAuthor ANN-PID (Artificial neural networks-PID) -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Computer Science -
Appears in Collections:
Marine Industry Research Division > Maritime ICT & Mobility Research Department > 1. Journal Articles
Sea Power Enhancement Research Division > Marine Domain & Security Research Department > 1. Journal Articles
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