A research team from National Korea Maritime & Ocean University in South Korea has developed a new control method that can optimize autonomous ship navigation.
Existing ship control systems using Model Predictive Control for Maritime Autonomous Surface Ships (MASS) do not consider the various forces acting on ships in real sea conditions, according to a team of researchers from National Korea Maritime & Ocean University.
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Addressing this gap, in a new study , they developed a new time-optimal control method, that accounts for the real wave loads acting on a ship, to enable effective planning and control of MASS at sea.
Designing a control system for time-efficient ship maneuvering is one of the most difficult challenges in autonomous ship control, the team noted. While many studies have investigated this problem and proposed various control methods, including MPC most have focused on control in calm waters, which do not represent real operating conditions. At sea, ships are continuously affected by different external loads, with loads from sea waves being the most significant factor affecting maneuvering performance.
“ Our control model accounts for various forces that act on the ship, enabling MASS to better navigate and track targets in dynamic sea conditions,” Assistant Professor Daejeong Kim from the Division of Navigation Convergence Studies at the Korea Maritime & Ocean University, who led the team, commented.
At the heart of this new control system is a comprehensive 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 used to optimize the maneuvering time. For this, the researchers developed a novel time optimization model that transforms the mathematical model from a temporal formulation to a spatial one. This successfully optimizes the maneuvering time, it was stated.
The models were integrated into a nonlinear MPC controller to achieve time-optimal control. They tested controller by simulating a real ship model navigating in the sea with different wave loads.
Additionally, for effective course planning and tracking 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 revealed that wave loads increased the expected maneuvering time on both strategies B and C. Comparing the two strategies, the researchers found strategy B to be simpler with lower performance than strategy C, with the latter being more reliable. However, strategy C places an additional burden on the controller by including wave load prediction in the planning stage.
“ Overall, our study addresses a critical gap in autonomous ship manoeuvring which could contribute to the development of a more technologically advanced maritime industry ,” Kim stated.
“ 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 .”
The study of ship maneuvering at sea has long been the central focus of the shipping industry. With the rapid advancements in remote control, communication technologies and artificial intelligence, the concept of MASS has emerged as a promising solution for autonomous marine navigation.
Recently, South Korean shipbuilder Hyundai Mipo Dockyard (HMD) completed the country’s first pilot vessel intended for autonomous navigation.
Pos Singapore, South Korea’s first demonstration ship intended for autonomous operation, will verify core autonomous operation technologies such as the intelligent navigation system and engine automation system being developed in Korea.
about 2 years ago
A research team from National Korea Maritime & Ocean University in South Korea has developed a new control method that can optimize autonomous ship navigation.
