Abstract
A three-degrees-of-freedom model, including surge, sway and yaw motion, with differential thrusters is proposed to describe unmanned surface vehicle (USV) dynamics in this study. The experiment is carried out in the Qing Huai River and the data obtained from different zigzag trajectories are filtered by a Gaussian filtering method. A physics-informed neural network (PINN) is proposed to identify the dynamic models of the USV. PINNs combine the advantages of data-driven machine learning and physical models. They can also embed the speed and steering models into the loss function, which can significantly retain all types of information. Compared with traditional neural networks, the results show that the PINN has better generalization ability in predicting the surge and sway velocities and rotation speed with only limited training data.
Highlights
Eng. 2022, 10, 148. https://doi.org/In recent years, the unmanned surface vehicle (USV) has attracted considerable attention
The physics-informed neural network (PINN) method was first proposed to identify the dynamic models of a USV
The speed model and steering model of the USV were embedded into the loss function which can guarantee the loss function, including the physical information
Summary
Eng. 2022, 10, 148. https://doi.org/In recent years, the unmanned surface vehicle (USV) has attracted considerable attention. The most important advantages of USVs are that they can be employed in extremely dangerous environments compared with traditional manned vehicles. To guarantee that USVs can operate with good performance in these fields, the utilization of robust and effective maneuvering controllers is important. Nagumo and Noda (1967) utilized continuous least square estimation based on an error-correction training procedure for system identification [5]. Holzhuter (1989) adopted recursive least square estimation in the identification of ship dynamics [6]. Kallstrom and Astrom (1981) demonstrated recursive maximum likelihood estimation used in the ship steering motion and showed a good prediction [7]. Yoon and Rhee (2003) proposed the extended Kalman filter technique and the modified Bryson–Frazier formulation smoother to predict motion variables, hydrodynamic force, vehicle speed and current direction [8]
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