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

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Title
Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network
Author(s)
Jeong, Sang Ki; Ji, Dae Hyeong; Oh, Ji Youn; Seo, Jungmin; Choi, Hyeung-sik
KIOST Author(s)
Jeong, Sang Ki(정상기)Ji, Dae Hyeong(지대형)Seo, Jungmin(서정민)
Alternative Author(s)
정상기; 지대형; 오지윤; 서정민
Publication Year
2021-04
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].
ISSN
1064-1246
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/39535
DOI
10.3233/JIFS-189622
Bibliographic Citation
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.40, no.4, pp.8001 - 8011, 2021
Publisher
IOS PRESS
Subject
Flow velocity; Long short-term memory; Ocean currents; Three term control systems; Unmanned surface vehicles; Control system modeling; Learning controllers; Marine research; Model algorithms; Movement speed; PID controllers; Recurrent neural network (RNN); System disturbances; Controllers
Keywords
USV (Unmanned surface vehicles); RNN (Recurrent neural network); LSTM (Long short-term memory models); ANN-PID (Artificial neural networks-PID)
Type
Article
Language
English
Document Type
Article
Publisher
IOS PRESS
Related Researcher
Research Interests

Marine Robot,Machine Design,해양로봇,기계설계

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