Korean researchers design optimised control method for autonomous vessels

A study outlining a new control method for optimisation of autonomous ship navigation has been published by a team of researchers at the Korea Maritime & Ocean University.

Led by Assistant Professor Kim Dae Jeong from the Department of Navigation Convergence Studies, the group has designed what it describes as a time-optimal control method for Maritime Autonomous Surface Ships (MASS), taking into account real operating conditions on manoeuvring performance, such as the continuous impact of different external loads from sea waves.

“Our control model accounts for various forces that act on the ship, enabling MASS to better navigate and track targets in dynamic sea conditions,” Dr Kim explains.

At the heart of the control system is a mathematical ship model that accounts for various forces in the sea, including wave loads, acting on key parts of a ship such as the hull, propellers, and rudders. However, this model cannot be directly applied to optimising manoeuvring, so the researchers also developed a time optimisation model that transforms the mathematical model from a temporal to a spatial formulation.

These two models were integrated into a nonlinear MPC controller to achieve time-optimal control, which was tested by simulating a real ship model navigating in the sea with different wave loads.

For effective course planning and tracking the researchers proposed three control strategies: Strategy A excluded wave loads during both the planning and tracking stages, serving as a reference; Strategy B included wave loads only in the planning stage, and Strategy C included wave loads in both stages, measuring their influence on both propulsion and steering.

Experiments showed that wave loads increased the expected manoeuvring time for both B and C. Comparing the two strategies, the researchers found B to be simpler but with lower performance than C, with the latter being more reliable. However, C places an additional burden on the controller by requiring wave load prediction in the planning stage.

“Our method enhances the efficiency and safety of autonomous vessel operations and potentially reduces shipping costs and carbon emissions, benefiting various sectors of the economy,” said Dr Kim.

“Overall, our study addresses a critical gap in autonomous ship manoeuvring which could contribute to the development of a more technologically advanced maritime industry.”

The study was published in Volume 293 of the journal Ocean Engineering and can be found here.

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Rob O'Dwyer

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