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  • 标题:Implementation and Evaluation of Non-linear Optimal Feedback Control for Ship’s Automatic Berthing by Recurrent Neural Network
  • 本地全文:下载
  • 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
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