基于 RBF 神经网络和滑模算法的船舶总段姿态调整过程建模与控制

IF 1.5 4区 工程技术 Q3 ENGINEERING, MARINE
Honggen Zhou, Chaojie Bao, Bo Deng, Lei Li
{"title":"基于 RBF 神经网络和滑模算法的船舶总段姿态调整过程建模与控制","authors":"Honggen Zhou, Chaojie Bao, Bo Deng, Lei Li","doi":"10.1177/14750902241227301","DOIUrl":null,"url":null,"abstract":"Due to the advantages such as high efficiency, high precision, and the ability to reduce welding distortion, the block assembly method in shipbuilding possesses currently holds a dominant position in shipbuilding engineering. However, some key issues including low adjustment precision and slow control response speed urgently need to be resolved for the block assembly adjustment technology. This paper committed to solving the problems of inaccurate tracking of target displacement and slow control response speed in the vertical motion axis of the ship block joining equipment. A docking equipment control method based on the RBF neural network coupled with adaptive sliding mode algorithm was proposed. Firstly, an overview of the overall mechanics and control architecture of the ship block joining equipment was provided. Subsequently, a mathematical model for the transmission at the lifting mechanism was established. A sliding mode controller based on position control for the ship block joining equipment was designed for the transmission system. Then, the RBF neural network was employed to adjust the switching gain of the sliding mode controller and develop a self-adaptive sliding mode controller. Finally, simulations and verifications were conducted for multiple sets of input trajectories with different types. The results demonstrated that the combination of the neural network algorithm and the sliding mode control algorithm model presented in this paper reduces the system response time by 28.125% and improves the average motion tracking accuracy by 30.76%.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":"255 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling and controlling of ship general section attitude adjustment process based on RBF neural network coupled with sliding mode algorithm\",\"authors\":\"Honggen Zhou, Chaojie Bao, Bo Deng, Lei Li\",\"doi\":\"10.1177/14750902241227301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the advantages such as high efficiency, high precision, and the ability to reduce welding distortion, the block assembly method in shipbuilding possesses currently holds a dominant position in shipbuilding engineering. However, some key issues including low adjustment precision and slow control response speed urgently need to be resolved for the block assembly adjustment technology. This paper committed to solving the problems of inaccurate tracking of target displacement and slow control response speed in the vertical motion axis of the ship block joining equipment. A docking equipment control method based on the RBF neural network coupled with adaptive sliding mode algorithm was proposed. Firstly, an overview of the overall mechanics and control architecture of the ship block joining equipment was provided. Subsequently, a mathematical model for the transmission at the lifting mechanism was established. A sliding mode controller based on position control for the ship block joining equipment was designed for the transmission system. Then, the RBF neural network was employed to adjust the switching gain of the sliding mode controller and develop a self-adaptive sliding mode controller. Finally, simulations and verifications were conducted for multiple sets of input trajectories with different types. The results demonstrated that the combination of the neural network algorithm and the sliding mode control algorithm model presented in this paper reduces the system response time by 28.125% and improves the average motion tracking accuracy by 30.76%.\",\"PeriodicalId\":20667,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment\",\"volume\":\"255 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/14750902241227301\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/14750902241227301","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
引用次数: 0

摘要

由于具有效率高、精度高、可减少焊接变形等优点,造船中的分块装配方法目前在造船工程中占据主导地位。然而,分块装配调整技术存在调整精度低、控制响应速度慢等亟待解决的关键问题。本文致力于解决船体拼装设备垂直运动轴目标位移跟踪不准确和控制响应速度慢的问题。提出了一种基于 RBF 神经网络和自适应滑模算法的船坞设备控制方法。首先,概述了船块连接设备的整体力学和控制结构。随后,建立了升降机构传动的数学模型。为传动系统设计了基于船体连接设备位置控制的滑动模式控制器。然后,采用 RBF 神经网络调节滑动模式控制器的开关增益,开发出一种自适应滑动模式控制器。最后,对多组不同类型的输入轨迹进行了模拟和验证。结果表明,本文提出的神经网络算法与滑动模式控制算法模型相结合,系统响应时间缩短了 28.125%,平均运动跟踪精度提高了 30.76%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and controlling of ship general section attitude adjustment process based on RBF neural network coupled with sliding mode algorithm
Due to the advantages such as high efficiency, high precision, and the ability to reduce welding distortion, the block assembly method in shipbuilding possesses currently holds a dominant position in shipbuilding engineering. However, some key issues including low adjustment precision and slow control response speed urgently need to be resolved for the block assembly adjustment technology. This paper committed to solving the problems of inaccurate tracking of target displacement and slow control response speed in the vertical motion axis of the ship block joining equipment. A docking equipment control method based on the RBF neural network coupled with adaptive sliding mode algorithm was proposed. Firstly, an overview of the overall mechanics and control architecture of the ship block joining equipment was provided. Subsequently, a mathematical model for the transmission at the lifting mechanism was established. A sliding mode controller based on position control for the ship block joining equipment was designed for the transmission system. Then, the RBF neural network was employed to adjust the switching gain of the sliding mode controller and develop a self-adaptive sliding mode controller. Finally, simulations and verifications were conducted for multiple sets of input trajectories with different types. The results demonstrated that the combination of the neural network algorithm and the sliding mode control algorithm model presented in this paper reduces the system response time by 28.125% and improves the average motion tracking accuracy by 30.76%.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.90
自引率
11.10%
发文量
77
审稿时长
>12 weeks
期刊介绍: The Journal of Engineering for the Maritime Environment is concerned with the design, production and operation of engineering artefacts for the maritime environment. The journal straddles the traditional boundaries of naval architecture, marine engineering, offshore/ocean engineering, coastal engineering and port engineering.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信