|Table of Contents|

Grey box identification modeling for ship maneuverability based on single parameter self-adjustable RM-GO-LSVR(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年02期
Page:
88-99
Research Field:
载运工具运用工程
Publishing date:

Info

Title:
Grey box identification modeling for ship maneuverability based on single parameter self-adjustable RM-GO-LSVR
Author(s):
MEI Bin1; 2;  SUN Li-cheng1;  SHI Guo-you1; 2;  MA Wen-yao3;  WANG Wei1; 2
(1. Navigation College, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. Key Laboratory of Navigation Safety of Liaoning Province, Dalian Maritime University, Dalian 116026, Liaoning, China; 3. Maritime College, Guangdong Ocean University, Zhanjiang 524088, Guangdong, China)
Keywords:
ship engineering;  ship maneuverability;  linear support vector regression;  identification modeling;  validation test
PACS:
U675.9
DOI:
10.19818/j.cnki.1671-1637.2020.02.008
Abstract:
To realize the identification modeling of ship maneuverability when the rudder angle is small and the test data were less, a grey box model for the ship maneuverability motion was put forward. The mathematical ship motion models with foregone hydrodynamic coefficients were collected as the alternative reference models(RM). The correlation coefficients between the identified ship and the alternative RMs were calculated to select the appropriate RM. The similitude regulation was applied to map the measurement data to the input range of RM and to build the motion relationship between the identified ship and the RM, and the accelerations of RM were acquired. The linear support vector regression(LSVR)machine was used to compensate the acceleration error between the identified ship and the RM. The mechanism model was analyzed, the suitable LSVR inputs were designed, and the global optimization(GO)algorithm was used to automatically adjust the insensitive band parameter of LSVR. The grey box model was trained by the data of free running model test, and the results were compared with those of the captive model test(CMT)and the computational fluid dynamics to validate the generalization ability and prediction accuracy. Research result shows that for the zigzag test with 20° heading angle and 20° rudder angle, the prediction accuracy of the first overshoot angle from the grey box model is at least 1° higher than those of CMT, virtual captive model test(VCMT)and RM method. The prediction accuracy of the second overshoot angle from the grey box model is at least 0.4° higher than those of CMT and VCMT. For the turning circle test with 35° rudder angle, the prediction accuracy of advance from the grey box model is at least 1% higher than those of CMT, VCMT, numerical circulating water channel test(NCWCT)and RM method. The prediction accuracy of tactical diameter from the grey box model is 4% less than that of CMT, and is 10% higher than that of NCWCT. The RM method is benefited for the grey box modeling. The GO algorithm can optimize the insensitive band parameters of LSVR. The established grey box method with self-adjustable single parameter can realize the identification modeling for the ship maneuverability with small rudder angle and less test data. 6 tabs, 7 figs, 36 refs.

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Last Update: 2020-05-22