Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm. (October 2017)

• Record Type: Journal Article
• Authors: Zhu, Man; Hahn, Axel; Wen, Yuan-Qiao; Bolles, Andre
• Is Part Of: Applied ocean research. Volume 68(2017)
• Page Start: 249
• Page End: 261
• Publication Date: 2017-10
• Subjects: Nonlinear ship dynamics -- Simplified ship models -- Large ships -- Support vector machines -- Artificial bee colony algorithm -- Parameter identification; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2017.09.006
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: The 6 DOF ship dynamic model is simplified into decoupled speed and steering models. The 4 DOF dynamic model of a large container ship simulates maneuvers for identification. LSSVM optimized by ABC is used as the parameter identification method. The suitable performance of simplified models is validated by comparing them with the response model. The superior optimization of ABC for SVM is proven by comparing with PSO and cross-validation method. Abstract: The 6 degrees of freedom (DOF) model with a high degree of complexity for capturing ship dynamics is generally able to track the nonlinear and coupling dynamics of ships. However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control. Therefore, simplified ship dynamic models within allowed accuracy are essential. This paper simplified the 6 DOF nonlinear dynamic model of ships into two decoupled models including the speed model and the steering model through reasonable assumptions. Those models were tested through maneuvering simulations of a container ship with a 4 DOF dynamic model. Support vector machines (SVM) optimized by the artificial bee colony algorithm (ABC) was used to identify parameters of speed and steering models by analyzing the rudder angle, propeller shaft speed, surge and sway velocities, and yaw rate from simulated data extracted from a series of maneuvers made by the container ship. Comparisons with the first order linear and nonlinear Nomoto models show that the simplified nonlinear steering model can capture more complicated dynamics and performs better. Additionally, comparisons among three different parameter identification methods demonstrate similar identification results but the different performance involving the applicability and effectiveness. SVM optimized by ABC is relatively convenient and effective for parameter identification of ship simplified dynamic models.