Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Abstract

Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of modeling-based control design. System identification from observed input-output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression, are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KVLCC2 ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics and prediction accuracy.

Application of an offline grey box method for predicting the manoeuvring performance

“…It is important to highlight that Milanov [29] and Efremov and Milanov [30] accessed the initial coefficients in TRR through PMM test. The study of Liu et al [31] presented an overview of the identification process. They compared the capabilities and challenges of offline grey and black box methods for determining the coefficients of Abkowitz's mathematical model of the the second variant of the MOERI tanker KRISO Very Large Crude Carrier (KVLCC2) using zigzag maneuver data.…”