[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121642-en":3,"doc-seo-121642-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121642,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Predictive Control of a Heaving Compensation System Based on Machine Learning Prediction Algorithm - Article","Floating structures are central to offshore engineering as operations move to deeper waters. Unexpected wave-induced motions degrade performance, while horizontal motions are mitigated by dynamic positioning and vertical heave motions are targeted by active heave compensation (AHC). The study develops a predictive control strategy using machine learning to reduce heave motion of a crane payload. Back propagation neural networks (BPNN) and LSTM recurrent neural networks predict compensation inputs, and predictive error is evaluated against actual-data feedforward cases, including regular and irregular waves.","Article  \nPredictive Control of a Heaving Compensation System Based on Machine Learning Prediction Algorithm  \nLifen Hu 1, Ming Zhang 2,*, Zhi-Ming Yuan 2, Hongxia Zheng 3 and Wenbin Lv 4  \nCitation: Hu, L.; Zhang, M.; Yuan, Z.-M.; Zheng, H.; Lv, W. Predictive Control of a Heaving Compensation System Based on Machine Learning Prediction Algorithm. J. Mar. Sci. Eng. 2023, 11, 821. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jmse11040821](10.3390/jmse11040821)  \nAcademic Editor: Jens Engström  \nReceived: 21 March 2023  \nRevised: 9 April 2023  \nAccepted: 11 April 2023  \nPublished: 12 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Ulsan Ship and Ocean College, Ludong University, Yantai 264025, China; [hulifen@ldu.edu.cn](hulifen@ldu.edu.cn)  \n2 Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0TD, UK; [zhiming.yuan@strath.ac.uk](zhiming.yuan@strath.ac.uk)  \n3 College of Transporation, Ludong University, Yantai 264025, China; [hongxiazheng@163.com](hongxiazheng@163.com)  \n4 Yantai Research Institute, Harbin Engineering University, Yantai 264006, China; [15684193627@163.com](15684193627@163.com)  \n* [Correspondence: ming.zhang.100@strath.ac.uk](Correspondence: ming.zhang.100@strath.ac.uk)  \nAbstract: Floating structures have become a major part of offshore structure communities as offshore engineering moves from shallow waters to deeper ones. Floating installation ships or platforms are widely used in these engineering operations. Unexpected wave-induced motions affect floating structures, especially in harsh sea conditions. Horizontal motions on the sea surface can be offset by a dynamic positioning system, and heave motions can be controlled by a heave compensation system. Active heave compensation (AHC) systems are applied to control vertical heave motions and improve safety and efficiency. Predictive control based on machine learning prediction algorithms further improves the performance of active heave compensation control systems. This study proposes a predictive control strategy for an active heave compensation system with a machine learning prediction algorithm to minimise the heave motion of crane payload. A predictive active compensation model is presented to verify the proposed predictive control strategy, and proportion􀂖integration􀂖differentiation control with predictive control is adopted. The reliability of back propagation neural network (BPNN) and long short-term memory recurrent neural network (LSTM RNN) prediction algorithms is proven. The influence of the predictive error on compensation performance is analysed by comparing predictive feedforward cases with actual-data feedforward cases. Predictive feedforward control with regular and irregular wave conditions is discussed, and the possible strategies are examined. After implementing the proposed predictive control strategy based on a machine learning algorithm in an active heave compensation system, the heave motion of the payload is reduced considerably. This investigation is expected to contribute to the motion control strategy of floating structures.  \nKeywords: active heave compensation; predictive control strategy; machine learning; back propagation neural network; long short-term memory recurrent neural network  \n1. Introduction  \nOffshore engineering has moved from shallow waters to deeper ones, and 􀀃oating structures have become a major part of offshore engineering communities. Ship and ocean engineering platforms are affected by wave load, so undesirable motions affect many engineering operations [1], including the equipment handling of oil and gas systems [2","cbCaitp9jY1s0oMH","https://ap.wps.com/l/cbCaitp9jY1s0oMH","pdf",3749184,1,18,"English","en",105,"# Introduction\n## Offshore motion and heave compensation\n## Passive vs active heave compensation\n## Crane payload safety and efficiency","[{\"question\":\"What problem does the predictive control approach address in active heave compensation systems?\",\"answer\":\"It aims to minimize the heave motion of a crane payload caused by unexpected wave-induced motions, improving safety and operational efficiency in harsh sea conditions.\"},{\"question\":\"Which machine learning prediction algorithms are used for the predictive compensation strategy?\",\"answer\":\"The approach verifies prediction reliability using a back propagation neural network (BPNN) and a long short-term memory recurrent neural network (LSTM RNN).\"},{\"question\":\"How is the impact of predictive error assessed?\",\"answer\":\"By comparing predictive feedforward cases with actual-data feedforward cases, the study analyzes how prediction errors influence compensation performance under regular and irregular wave conditions.\"}]","Predictive Control of a Heaving Compensation System Based on Machine Learning Prediction Algorithm - 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