Parameter Identification of an Unmanned Sailboat Combining Experiments and Numerical Analysis

It is meaningful to develop an accurate model to predict the dynamical motion of an unmanned sailboat. Considering cost and convenience, this work proposes a parameter identification method based on the combination of experiments and numerical analysis. Firstly, a free-running trial is carried out by utilizing the propellers on the studied sailboat to acquire real navigation information. Secondly, particle swarm optimization (PSO), which is highly efficient and easily implemented, is designed to acquire the hydrodynamic parameters of the sailboat’s hull. At the same time, the aerodynamic parameters of the sail are acquired by computational fluid dynamics (CFD) simulation. Finally, a three degree-of-freedom (DOF) model is established, the effectiveness of which is verified through comparisons between sea trials and simulation. The results prove that this parameter identification method has the desired accuracy and reliability.