文章基本信息

标题：Implementation and Evaluation of Non-linear Optimal Feedback Control for Ship’s Automatic Berthing by Recurrent Neural Network
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作者：Naoki Mizuno ; Ryo Kuboshima
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：21
页码：91-96
DOI：10.1016/j.ifacol.2019.12.289
语种：English
出版社：Elsevier
摘要：In this paper, we present an automatic ship’s berthing system by non-linear optimal feedback controller. In the proposed method, the recurrent neural network is used for non-linear optimal feedback controller for the realistic operational conditions such as different berthing distances and different disturbances. To obtain the feasible non-linear controller, the recurrent neural network is trained using pre-computed non-linear optimal solutions for various conditions. In order to evaluate the performance of the proposed system, extensive computer simulations and actual sea tests are carried out using small training shipShioji-Maruunder various conditions. As a result, we can see that the proposed non-linear optimal feedback controller by recurrent neural network is useful for automatic berthing system for the ship.
关键词：Keywordsship controlautomatic berthingnon-linear optimal controlfeedback controlrecurrent neural networkmachine learning