{"title":"NeuroVI-based wave compensation system control for offshore wind turbines.","authors":"Fengshuang Ma, Xiangyong Liu, Zhiqiang Xu, Tianhong Ding","doi":"10.3389/fnbot.2025.1648713","DOIUrl":null,"url":null,"abstract":"<p><p>In deep-sea areas, the hoisting operation of offshore wind turbines is seriously affected by waves, and the secondary impact is prone to occur between the turbine and the pile foundation. To address this issue, this study proposes an integrated wave compensation system for offshore wind turbines based on a neuromorphic vision (NeuroVI) camera. The system employs a NeuroVI camera to achieve non-contact, high-precision, and low-latency displacement detection of hydraulic cylinders, overcoming the limitations of traditional magnetostrictive displacement sensors, which exhibit slow response and susceptibility to interference in harsh marine conditions. A dynamic simulation model was developed using AMESim-Simulink co-simulation to analyze the compensation performance of the NeuroVI-based system under step and sinusoidal wave disturbances. Comparative results demonstrate that the NeuroVI feedback system achieves faster response times and superior stability over conventional sensors. Laboratory-scale model tests and real-world application in the installation of a 5.2 MW offshore wind turbine validated the system's feasibility and robustness, enabling real-time collaborative control of turbine and cylinder displacement to effectively mitigate multi-impact risks. This research provides an innovative approach for deploying neural perception technology in complex marine scenarios and advances the development of neuro-robotic systems in ocean engineering.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1648713"},"PeriodicalIF":2.8000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343490/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neurorobotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3389/fnbot.2025.1648713","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In deep-sea areas, the hoisting operation of offshore wind turbines is seriously affected by waves, and the secondary impact is prone to occur between the turbine and the pile foundation. To address this issue, this study proposes an integrated wave compensation system for offshore wind turbines based on a neuromorphic vision (NeuroVI) camera. The system employs a NeuroVI camera to achieve non-contact, high-precision, and low-latency displacement detection of hydraulic cylinders, overcoming the limitations of traditional magnetostrictive displacement sensors, which exhibit slow response and susceptibility to interference in harsh marine conditions. A dynamic simulation model was developed using AMESim-Simulink co-simulation to analyze the compensation performance of the NeuroVI-based system under step and sinusoidal wave disturbances. Comparative results demonstrate that the NeuroVI feedback system achieves faster response times and superior stability over conventional sensors. Laboratory-scale model tests and real-world application in the installation of a 5.2 MW offshore wind turbine validated the system's feasibility and robustness, enabling real-time collaborative control of turbine and cylinder displacement to effectively mitigate multi-impact risks. This research provides an innovative approach for deploying neural perception technology in complex marine scenarios and advances the development of neuro-robotic systems in ocean engineering.
期刊介绍：
Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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