Nonlinear innovation algorithm for unmanned surface vessel  identification based on comprehensive application method

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  • (Navigation and Ship Engineering College, Dalian Ocean University, Dalian 116023,China)

Online published: 2024-05-17

Abstract

To avoid the phenomenon of parameter drift caused by dynamic cancellation in identification modeling stage of unmanned surface vessel (USV), two measures of parallel processing and estimation of too large and too small initial values were combined to form a comprehensive application method, and on this basis, a nonlinear innovation identification algorithm was proposed. Experimental results show that the proposed algorithm avoids the drift phenomenon of hydrodynamic coefficients and improves the ability to reprocess historical data. The algorithms have the characteristics of strong solving ability, high identification efficiency, and low computational burden, laying the foundation for stable navigation of USV in interference environments.

Cite this article

SUI Jianghua, LI Yinfu, SONG Chunyu . Nonlinear innovation algorithm for unmanned surface vessel  identification based on comprehensive application method[J]. Journal of Dalian Maritime University, 2024 , 50(3) : 97 -103 . DOI: 10.16411/j.cnki.issn1006-7736.2024.03.011

References

[1]刘勇.交通基础设施投资、区域经济增长及空间溢出作用——基于公路、水运交通的面板数据分析[J].中国工业经济,2010(12):37-46.
LIU Y. Transport infrastructure investment, regional economic growth and spatial spillover effects-Panel data analysis based on road and waterway transport [J]. China Industrial Economy, 2010 (12): 37-46. (in Chinese)
[2]罗伟林,李铁山,邹早建.船舶操纵运动建模中的参数可辨识性问题[J].大连海事大学学报,2009,35(4):1-3.
LUO W L, LI T S, ZOU Z J. Parameter identifiability in ship maneuvering motion modeling [J] Journal of Dalian Maritime University, 2009,35 (4): 1-3. (in Chinese)
[3]LI M H, LIU X M. Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems[J]. International Journal of Adaptive Control and Signal Processing, 2021, 35(2): 240-261.
[4]PAN J, ZHANG H J, GUO H Z, et al. Multivariable CAR-like system identification with multi-innovation gradient and least squares algorithms[J]. International Journal of Control, Automation and Systems, 2023, 21(5): 1455-1464.
[5]ZHANG X K, ZHAO B G, ZHANG G Q. Improved parameter identification algorithm for ship model based on nonlinear innovation decorated by sigmoid function[J]. Transportation Safety and Environment, 2021, 3(2): 114-122.
[6]彭颢文.基于EKF实时辨识的船舶航迹跟踪控制[D].大连:大连海事大学,2023.000625.
PENG H W. Ship track tracking control based on EKF real-time identification [D].Dalian:Dalian Maritime University, 2023. 000625. (in Chinese)
[7]丁锋.系统辨识(6):多新息辨识理论与方法[J].南京信息工程大学学报(自然科学版),2012,4(1):1-28.
DING F. Theory and method of multi-innovation identification[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2012, 4(1): 1-28. (in Chinese).
[8]CHAUDHARY N I, RAJA M A Z, HE Y, et al. Design of multi-innovation fractional LMS algorithm for parameter estimation of input nonlinear control autoregressive systems[J]. Applied Mathematical Modelling, 2021, 93: 412-425.
[9]The maneuvering committee of ITTC, final report and recommendations to the 25th ITTC[C].25th International Towing Tank Conference, Fukuoka, Japan,2008:143–208.
[10]SONG C Y, ZHANG X K, ZHANG G Q. Attitude prediction of ship coupled heave–pitch motions using nonlinear innovation via full-scale test data[J]. Ocean Engineering, 2022, 264: 112524.
[11]SHENOI R.R, KRISHNANKUTTY P, SELVAM, R. P. Sensitivity study of hydrodynamic derivative variations on the maneuverability prediction of a container ship[R]. In: ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering (OMAE2015), American Society of Mechanical Engineers, 2015.
[12]LUO W L, LI X Y. Measures to diminish the parameter drift in the modeling of ship maneuvering using system identification[J]. Applied Ocean Research, 2017, 67: 9-20.
[13]WANG S, WANG L J, IM N, et al. Real-time parameter identification of ship maneuvering response model based on nonlinear gaussian filter[J]. Ocean Engineering, 2022, 247: 110471.
[14]SONG C Y, ZHANG X K, ZHANG G Q. Nonlinear identification for 4-DOF ship maneuvering modeling via full-scale trial data[J]. IEEE Transactions on Industrial Electronics, 2021, 69(2): 1829-1835.
[15]龚涛,董早鹏.基于RLSM的海洋无人航行器操纵性参数辨识[J].大连海事大学学报,2019,45(2):50-57.
GONG T, DONG Z P. RLSM-based maneuvering parameter identification of marine unmanned aerial vehicles [J]. Journal of Dalian Maritime University, 2019,45 (2): 50-57. (in Chinese)
[16] SUN X J, WANG G F, FAN Y S. Model identification and trajectory tracking control for vector propulsion unmanned surface vehicles[J]. Electronics, 2019, 9(1): 22.

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