Model Reference Adaptive Control-Based Autonomous Berthing of an Unmanned Surface Vehicle under Environmental Disturbance

Surface-vehicle berthing is a complex and challenging task. It involves complicated ship dynamics owing to a large drift angle and the time-varying environmental disturbances induced by wind, wave, and current. Therefore, berthing requires state processing at each step and a controller that can respond to the non-linear behavior of the unmanned surface vehicle (USV). Herein, a systematic approach for the autonomous berthing of a USV is proposed. A state-machine approach is proposed to solve state transitions in the berthing step. A model reference adaptive controller was adopted to cope with the uncertainty of USV dynamics during berthing. Herein, the theoretical background and design of the adaptive controller are described. Simulation for the validation of the proposed method was conducted using the robot operating system Gazebo-based simulator. Finally, the respective performances of the proposed method and conventional controller were compared. Using the proposed berthing approach, an accurate and stable docking of small USVs became achievable.

Automated summary

This paper proposes a systematic approach for autonomous berthing of unmanned surface vehicles (USVs). It uses a state-machine approach to handle state transitions in the berthing step, and adopts a model reference adaptive controller to cope with the uncertainty of USV dynamics. Simulation using the Gazebo-based simulator validates the proposed method and compares its performance with a conventional controller, demonstrating that the proposed method enables accurate and stable docking.