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Research on Adaptive Control for Automatic Ship Berthing Based on Physics-Informed Neural Networks
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Shengchao ZHANG1, 2, Junjie GAO3, Fei YIN4, Zhigang LIU3, Wanyou LI1
Ship Engineering | 2026, 48(3) : Z29 - Z38
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Ship Engineering | 2026, 48(3): Z29-Z38
• Special Topic: Intelligent Ship •
Research on Adaptive Control for Automatic Ship Berthing Based on Physics-Informed Neural Networks
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Shengchao ZHANG1, 2, Junjie GAO3, Fei YIN4, Zhigang LIU3, Wanyou LI1
Affiliations
  • College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
  • Xiamen Port Shipping Co,Ltd, Xiamen 361012, China
  • Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
  • Shanghai Marine Equipment Research Institute, Shanghai 200031, China
Published: 2026-03-25 doi: 10.13788/j.cnki.cbgc.2026.03.Z2
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[Purpose]

Existing autonomous berthing technologies rely on precise mathematical ship models and mostly employ empirical formulas for modeling. However, in actual berthing scenarios, influenced by environmental factors and speed, these methods cannot accurately reflect the current ship maneuvering status in real-time, leading to limited berthing control accuracy. To address the aforementioned problems,

[Method]

a ship autonomous berthing control method based on physics- informed neural networks (PINN) is proposed. The method constructs a real-time dataset using a sliding window and identifies ship maneuvering parameters in real-time through the physics-informed neural network. An adaptive controller based on gain scheduling is designed to dynamically adjust control gains using the identified parameters, realizing precise ship berthing.

[Result]

Experimental results demonstrate that the PINN network can converge rapidly under dynamic conditions and accurately identify ship parameters, with a goodness of fit reaching 0.97. In berthing experiments, the method ensured that the terminal heading deviation and lateral error converged to a minimal range, achieving smooth and safe docking, with a heading error of 0.13°.

[Conclusion]

The method effectively resolves the failure of traditional control algorithms caused by model mismatch under unknown ship parameters and complex working conditions, offering a safe and interpretable adaptive berthing control solution.

physics-informed neural network (PINN)  /  autonomous berthing  /  maritime autonomous surface ship (NASS)  /  adaptive control
Shengchao ZHANG, Junjie GAO, Fei YIN, Zhigang LIU, Wanyou LI. Research on Adaptive Control for Automatic Ship Berthing Based on Physics-Informed Neural Networks[J]. Ship Engineering, 2026 , 48 (3) : Z29 -Z38 . DOI: 10.13788/j.cnki.cbgc.2026.03.Z2
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Year 2026 volume 48 Issue 3
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doi: 10.13788/j.cnki.cbgc.2026.03.Z2
  • Online Date：2026-04-24
  • Published：2026-03-25
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Affiliations
    College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
    Xiamen Port Shipping Co,Ltd, Xiamen 361012, China
    Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
    Shanghai Marine Equipment Research Institute, Shanghai 200031, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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