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Control system research in wave compensation based on particle swarm optimization
For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319418/ https://www.ncbi.nlm.nih.gov/pubmed/34321502 http://dx.doi.org/10.1038/s41598-021-93973-4 |
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author | Tang, Gang Lu, Peng Hu, Xiong Men, Shaoyang |
author_facet | Tang, Gang Lu, Peng Hu, Xiong Men, Shaoyang |
author_sort | Tang, Gang |
collection | PubMed |
description | For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems. |
format | Online Article Text |
id | pubmed-8319418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83194182021-07-29 Control system research in wave compensation based on particle swarm optimization Tang, Gang Lu, Peng Hu, Xiong Men, Shaoyang Sci Rep Article For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems. Nature Publishing Group UK 2021-07-28 /pmc/articles/PMC8319418/ /pubmed/34321502 http://dx.doi.org/10.1038/s41598-021-93973-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tang, Gang Lu, Peng Hu, Xiong Men, Shaoyang Control system research in wave compensation based on particle swarm optimization |
title | Control system research in wave compensation based on particle swarm optimization |
title_full | Control system research in wave compensation based on particle swarm optimization |
title_fullStr | Control system research in wave compensation based on particle swarm optimization |
title_full_unstemmed | Control system research in wave compensation based on particle swarm optimization |
title_short | Control system research in wave compensation based on particle swarm optimization |
title_sort | control system research in wave compensation based on particle swarm optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319418/ https://www.ncbi.nlm.nih.gov/pubmed/34321502 http://dx.doi.org/10.1038/s41598-021-93973-4 |
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