주성분의 선형 결합을 통한 다변수 식별에 기반한 선박 조종 운동 모델 개발
Ship Dynamics Modeling Based on Multivariate Identification by Linear Combination of Principal Components
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The present study suggests a data-driven multivariate identification method based on principal component analysis and shows an application to ship dynamics modeling in maneuver. A reduced order model of ship dynamics is built by linear combination of three principal components acquired from large angle zigzag maneuver test. For a given kinematic state with three variables, a proper span is found by least square method, therefore accompanying hydrodynamic force and moment is determined. Suggested dynamics model correctly estimates hydrodynamic force and moment, thus it showed good agreement in maneuver simulation with that of conventional ship dynamics model obtained by system identification of captive model tests.
Abstract
The present study suggests a data-driven multivariate identification method based on principal component analysis and shows an application to ship dynamics modeling in maneuver. A reduced order model of ship dynamics is built by linear combination of three principal components acquired from large angle zigzag maneuver test. For a given kinematic state with three variables, a proper span is found by least square method, therefore accompanying hydrodynamic force and moment is determined. Suggested dynamics model correctly estimates hydrodynamic force and moment, thus it showed good agreement in maneuver simulation with that of conventional ship dynamics model obtained by system identification of captive model tests.
참고문헌 (21)
- Araki, M., "Estimating maneuvering coefficients using system identification methods with experimental, system-based and CFD free-running trial data", Ocean Engineering, 51, pp.63~84 (2012)
- Bishop, C. M., "Pattern recognition and machine learning" (2006), Springer Nature
- Bonci, M., "Method for estimating parameters of practical ship manoeuvring models based on the combination of RANSE computations and system identification", Applied Ocean Research, 52, pp.274~294 (2015)
- Brunton, S. L., "Data-driven science and engineering : machine learning, dynamical systems, and control" (2019), Cambridge University Press
- Chillcce, G., "Data-driven system identification of hydrodynamic maneuvering coefficients from free-running tests", Physics of Fluids, 35(5) (2023)
- Jian, C., "Parametric estimation of ship maneuvering motion with integral sample structure for identification", Applied Ocean Research, 52, pp.212~221 (2015)
- Kim, D., "A study on 4DOF ship dynamics in maneuver by principal component analysis", Journal of the Society of Naval Architects of Korea, 61(1), pp.29~44 (2024)
- Lever, J., "Points of significance : principal component analysis", Nature methods, 14(7), pp.641~643 (2017)
- Lewandowski, E. M., "The dynamics of marine craft : maneuvering and seakeeping" (2004), World scientific Publishing CO. Pte. Ltd
- Liu, S. Y., "Black-box modeling of ship maneuvering motion based on Gaussian progress regression optimized by particle swarm optimization", International Journal of Offshore and Polar Engineering, 33(4), pp.337~345 (2023)
- Lou, J., "Deep learning method for 3-DOF motion prediction of unmanned surface vehicles based on real sea maneuverability test", Ocean Engineering, 250 (2022)
- Luo, W., "Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification", Applied Ocean Research, 67, pp.9~20 (2017)
- Ouyang, Z. L., "Adaptive hybrid-kernel function based Gaussian process regression for nonparametric modeling of ship maneuvering motion", Ocean Engineering, 268 (2023)
- Ouyang, Z. L., "Identification modeling of ship maneuvering motion based on local Gaussian process regression", Ocean Engineering, 267 (2023)
- Sutulo, S., "An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests", Ocean Engineering, 79, pp.10~25 (2014)
- Wakita, K., "On neural network identification for low-speed ship maneuvering model", Journal of Marine Science and Technology, 22, pp.772~785 (2022)
- Wang, Z., "Identification of ship manoeuvring motion based on nu-support vector machine", Ocean Engineering, 183, pp.270~281 (2019)
- Wang, T., "Incorporating approximate dynamics into data-driven calibrator : A representative model for ship maneuvering prediction", IEEE Transactions on Industrial Informatics, 18(3), pp.1781~1789 (2022)
- Xue, Y., "Hydrodynamic parameter identification for ship manoeuvring mathematical models using a Bayesian approach", Ocean Engineering, 195 (2020)
- Yasukawa, H., "Introduction of MMG standard method for ship maneuvering predictions", Journal of Marine Science and Technology, 22, pp.37~52 (2015)
- Zhang, X. G., "Identification of Abkowitz model for ship manoeuvring motion using ε-support vector regression", Journal of hydrodynamics, 23(3), pp.353~360 (2011)