{"title":"深海载人航行器动态性能及运动控制方法研究。","authors":"Dejun Li, Qiaosheng Zhao, Chunrong He, Wei Zhang, Shaocheng Li, Shenshen Yang, Xinbei Lv","doi":"10.1038/s41598-025-90049-5","DOIUrl":null,"url":null,"abstract":"<p><p>The deep-sea human occupied vehicles (HOV) typically have the characteristics such as large dimensions, complex structures, and significant nonlinearities of their dynamic models, which makes it difficult for the operator to precisely control the vehicle for completing the tasks such as target tracking, grabbing, and specific area exploration. Establishing an accurate hydrodynamic model and obtaining an appropriate control algorithm are the key to solving the aforementioned problems. Therefore, this paper takes the 7000-meter deep-sea human occupied vehicle \"Jiaolong\" as the research object. The accurate hydrodynamic coefficients of the vehicle are obtained based on the maneuvering test data of the vehicle, and a complete six-degree-of-freedom model of the vehicle is established. The precision of the dynamic model is verified by the test data of Pacific deep submerging. The simulation studies of typical maneuvering conditions are conducted to demonstrate that the vehicle possesses good maneuverability. By utilizing the dynamic model, the control strategy for the HOV is derived using the control algorithm of adaptive integral sliding mode (AISMC). The simulations confirm that this control method can significantly enhance the control accuracy of the vehicle, and the challenges posed by model uncertainties of the model and external disturbances of the environment are effectively addressed.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"5821"},"PeriodicalIF":3.9000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832742/pdf/","citationCount":"0","resultStr":"{\"title\":\"Research on the dynamic performance and motion control methods of deep-sea human occupied vehicles.\",\"authors\":\"Dejun Li, Qiaosheng Zhao, Chunrong He, Wei Zhang, Shaocheng Li, Shenshen Yang, Xinbei Lv\",\"doi\":\"10.1038/s41598-025-90049-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The deep-sea human occupied vehicles (HOV) typically have the characteristics such as large dimensions, complex structures, and significant nonlinearities of their dynamic models, which makes it difficult for the operator to precisely control the vehicle for completing the tasks such as target tracking, grabbing, and specific area exploration. Establishing an accurate hydrodynamic model and obtaining an appropriate control algorithm are the key to solving the aforementioned problems. Therefore, this paper takes the 7000-meter deep-sea human occupied vehicle \\\"Jiaolong\\\" as the research object. The accurate hydrodynamic coefficients of the vehicle are obtained based on the maneuvering test data of the vehicle, and a complete six-degree-of-freedom model of the vehicle is established. The precision of the dynamic model is verified by the test data of Pacific deep submerging. The simulation studies of typical maneuvering conditions are conducted to demonstrate that the vehicle possesses good maneuverability. By utilizing the dynamic model, the control strategy for the HOV is derived using the control algorithm of adaptive integral sliding mode (AISMC). The simulations confirm that this control method can significantly enhance the control accuracy of the vehicle, and the challenges posed by model uncertainties of the model and external disturbances of the environment are effectively addressed.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"5821\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832742/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-90049-5\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-90049-5","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Research on the dynamic performance and motion control methods of deep-sea human occupied vehicles.
The deep-sea human occupied vehicles (HOV) typically have the characteristics such as large dimensions, complex structures, and significant nonlinearities of their dynamic models, which makes it difficult for the operator to precisely control the vehicle for completing the tasks such as target tracking, grabbing, and specific area exploration. Establishing an accurate hydrodynamic model and obtaining an appropriate control algorithm are the key to solving the aforementioned problems. Therefore, this paper takes the 7000-meter deep-sea human occupied vehicle "Jiaolong" as the research object. The accurate hydrodynamic coefficients of the vehicle are obtained based on the maneuvering test data of the vehicle, and a complete six-degree-of-freedom model of the vehicle is established. The precision of the dynamic model is verified by the test data of Pacific deep submerging. The simulation studies of typical maneuvering conditions are conducted to demonstrate that the vehicle possesses good maneuverability. By utilizing the dynamic model, the control strategy for the HOV is derived using the control algorithm of adaptive integral sliding mode (AISMC). The simulations confirm that this control method can significantly enhance the control accuracy of the vehicle, and the challenges posed by model uncertainties of the model and external disturbances of the environment are effectively addressed.
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