Data-Driven Modeling of Unmanned Surface Vehicle's Maneuvering Motion Based on Real Navigational Data
This study conducts a data-driven modeling research of the"Jinghai"unmanned surface vehicle based on lake trial data.The objective is to develop a kinetic model that captures the actual characteristics of the vehicle.The framework incorporates the nonlinear hydrodynamic model structure and the model of the propulsion system as prior knowledge constraints.Support vector regression is employed to complete the modeling process using a training dataset excited by a sequence of random rudder angles.The study places a particular emphasis on assessing the feasibility of modeling with environmental disturbances.The results demonstrate that the proposed method enables rapid construction of a nonlinear model for unmanned surface vehicles.The prior model structure effectively mitigates the impact of waves and currents in navigational data.The model accurately predicts the motion states during turning and zigzag trials,and exhibits good generalization ability.
unmanned surface vehiclemaneuverabilitydata-drivenidentification modelingnavigation data
NETL
NSTL
万方数据
