Intelligent towing and pushing system for unmanned tugboats under wind and wave disturbances

ABSTRACT

Intelligent unmanned tugboats have the potential to significantly improve the efficiency of barge manipulation. This paper investigates the kinetic modelling, optimal force distribution, and cooperative control within the unmanned tugboat towing and pushing system. A practical kinetic model for the towing and pushing system has been developed, precisely taking into account factors like physical cable forces and disturbances caused by wind and waves in turbulent sea conditions. Following this, an optimal force allocation approach is investigated to allocate the desired force and determine the towing and pushing angles. Furthermore, a cooperative model predicted control strategy is proposed for the dual unmanned tugboats' towing and pushing systems. This strategy aims to ensure the barge quickly reaches its designated location, maintaining the desired heading and velocity. Simulation results indicate that unmanned tugboats can collaboratively tow and push the unpowered barge to the specified location with the desired heading and speed.

KEYWORDS:

• Unmanned surface vessels
• cooperative control
• multi-agent system
• ships guidance
• model predicted control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [Weidong Zhang], upon reasonable request.

Additional information

Funding

This work is supported in part by the National Key R&D Program of China under Grant 2022ZD0119903, in part by the National Natural Science Foundation of China under Grant U2141234, in part by the Taicang Foundation Research Grant TC2023JC25, in part by the Shanghai Science and Technology program under Grant 19510745200, 22015810300, in part by the Open Research Subject of State Key Laboratory of Intelligent Game Grant ZBKF-24-08 and Hainan Province Science and Technology Special Fund [ZDYF2021GXJS041].

Notes on contributors

Bin Du

Bin Du is with the Ocean Institute, Northwestern Polytechnical University, Taicang, 215400, China.

Weidong Zhang

Weidong Zhang is with Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.