Diving dynamics identification and motion prediction for marine crafts using field data  

作　　者：Yiming Zhong Caoyang Yu Yulin Bai Zheng Zeng Lian Lian 

机构地区：[1]School of Oceanography,Shanghai Jiao Tong University,Shanghai 200030,China [2]Key Laboratory of Polar Ecosystem and Climate Change,Ministry of Education,Shanghai Jiao Tong University,Shanghai 200030,China [3]Shanghai Key Laboratory of Polar Life and Environment Sciences,Shanghai Jiao Tong University,Shanghai 200030,China [4]State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Journal of Ocean Engineering and Science》2024年第4期391-400,共10页海洋工程与科学（英文）

基　　金：supported in part by the National Natural Sci-ence Foundation of China under Grant 42376187;in part by the National Key R&D Program of China under Grant 2023YFC2812800,in part by the Natural Science Foundation of Shanghai under Grant 22ZR1434600;in part by the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under Grant SL2022MS016;in part by the Shanghai Jiao Tong University 2030 Initiative under Grant WH510244001;in part by the Shanghai Underwater Robot En-gineering Technology Innovation Center under Grant 21DZ2221600.

摘　　要：Ensuring accurate parameter identification and diving motion prediction of marine crafts is essential for safe navigation,optimized operational efficiency,and the advancement of marine exploration.Addressing this,this paper proposes an instrumental variable-based least squares(IVLS)algorithm.Firstly,aiming to balance complexity with accuracy,a decoupled diving model is constructed,incorporating nonlinear actuator characteristics,inertia coefficients,and damping coefficients.Secondly,a discrete parameter identification matrix is designed based on this dedicated model,and then a IVLS algorithm is innovatively derived to reject measurement noise.Furthermore,the stability of the proposed algorithm is validated from a probabilistic point of view,providing a solid theoretical foundation.Finally,performance evaluation is conducted using four depth control datasets obtained from a piston-driven profiling float in Qiandao Lake,with desired depths of 30 m,40 m,50 m,and 60 m.Based on the diving dynamics identification results,the IVLS algorithm consistently shows superior performance when compared to recursive weighted least squares algorithm and least squares support vector machine algorithm across all depths,as evidenced by lower average absolute error(AVGAE),root mean square error(RMSE),and maximum absolute error values and higher determination coefficient(R2).Specifically,for desired depth of 60 m,the IVLS algorithm achieved an AVGAE of 0.553 m and RMSE of 0.655 m,significantly outperforming LSSVM with AVGAE and RMSE values of 8.782 m and 11.117 m,respectively.Moreover,the IVLS algorithm demonstrates a remarkable generalization capability with R2 values consistently above 0.95,indicating its robustness in handling varied diving dynamics.

关 键 词：Marine craft Parameter identification Motion prediction Instrumental variable-based least-squares algorithm Diving dynamics model 

分 类 号：U66[交通运输工程—船舶及航道工程] P73[交通运输工程—船舶与海洋工程]