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Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
文献类型:期刊论文
作者:Zhang, Wenjie[1]  Wu, Pin[2]  Peng, Yan[3]  Liu, Dongke[4]  
机构:[1]Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.;
[2]Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.;
[3]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China.;
[4]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China.;
通讯作者:Wu, P (reprint author), Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.
年:2019
期刊名称:FUTURE INTERNET
卷:11
期:11
增刊:正刊
收录情况:EI(20194807740195)  ESCI(WOS:000502277600023)  
所属部门:机电工程与自动化学院;计算机工程与科学学院
语言:外文
人气指数:10
浏览次数:10
基金:National Science Foundation for Distinguished Young Scholars of P. R. ChinaNational Natural Science Foundation of ChinaNational Science Fund for Distinguished Young Scholars [61525305]; State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center [SKLA20180303]; Natural Science Foundation of ShanghaiNatural Science Foundation of Shanghai [19ZR1417700]; Project of Shanghai Municipal Science and Technology Commission [17DZ1205000]
关键词:CNN; data-driven; LSTM; roll motion prediction; unmanned surface vehicle
摘要:
The prediction of roll motion in unmanned surface vehicles (USVs) is vital for marine safety and the efficiency of USV operations. However, the USV roll motion at sea is a complex time-varying nonlinear and non-stationary dynamic system, which varies with time-varying environmental disturbances as well as various sailing conditions. The conventional methods have the disadvantages of low accuracy, poor robustness, and insufficient practical application ability. The rise of deep learning provides ...More
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