Abstract
In view of the vulnerability of ocean unmanned sailboats to the large lateral velocities due to wind and waves during navigation, this paper proposes a Gaussian Process Model Predictive Control (GPMPC) method based on data-driven learning technique to improve the navigation tracking accuracy of unmanned sailboats. The feature model of the sailing course change subject to the wind and waves is learned from the efficient sampling data. It is then combined with the model predictive control to form the course controller. To reduce the influence of wind and waves disturbances, an adaptive weight term is designed in the object function to improve the tracking accuracy of the model predictive control. The guidance commands received by the model predictive controller take into account the path deviation caused by the current and lateral motion of the ship. The results show that GPMPC has the advantages of fast response time and less overshoot; the unmanned sailboat can better achieve waypoint tracking by learning navigation data.
Highlights
BackgroundWith the exhaustion of terrestrial resources, the explored and exploitation of the ocean have gained increasing attention from researchers, governments and the military
Compared with PID and Back Propagation neural network Model Predictive Control (BPMPC), the Gaussian Process Model Predictive Control (GPMPC) method itself can cope with different interference conditions brought by different heading control targets, and the control method has better flexibility, which is convenient for modifying the control objective function to enhance the tracking performance
The trajectory tracking problem of an unmanned sailboat is studied in terms of both the course control and navigation control
Summary
With the exhaustion of terrestrial resources, the explored and exploitation of the ocean have gained increasing attention from researchers, governments and the military. The harsh and changeable marine environment, as well as the competitive situation, pose challenges to the continuous marine monitoring. In this context, an Unmanned Surface Vehicle (USV) as a maritime vehicle becomes in urgent need. The additional sail structure required for wind power drive make the motion characteristics and kinetics model of the sailboat complicated. The magnitude and direction of the sailing speed are directly affected by the wind speed and direction. It results in a re-design for the navigation approach of the sailboat. Considering the uniqueness of sailboats, the controller developed for the conventional USV cannot be applied to the sailboat directly, which brings new directions and challenges to the research of sailboat control
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