Intelligent Vessel Course Stabilization Control System

ABSTRACT: This article presents the development of an intelligent vessel course stabilization control system using an artificial neural network. The objective of the study was to improve vessel control efficiency under external disturbances. A neural network capable of adapting to changing navigational conditions and compensating for external disturbances was used to implement the controller in the vessel course stabilization control system. Simulation of the developed vessel’s automatic course stabilization control system (ACS) was conducted in MATLAB/Simulink using mathematical models of vessel dynamics and wave disturbances. The simulation permit to analyze the effects of sea waves of varying intensity and interactions with oncoming vessels. A comparative analysis of the transient processes of the developed neural network-based system and a traditional adaptive course stabilization system with a PID controller was conducted. A combined method for controlling the vessel course stabilization system is proposed, including the use of a PID controller during stable navigation and automatic switching to a neural network controller when challenging operating conditions and intense external disturbances arise. The results of computer simulations conducted by the authors showed that the developed intelligent automatic control system ensures stable stabilization of the vessel's course when exposed to disturbances of varying intensity. The use of an intelligent approach reduced the transition time and minimized deviations from the set course. The trained neural controller demonstrated high adaptability and resilience to changing parameters of a complex navigational environment.

KEYWORDS: Automatic Control, Neural Networks, Navigation Safety, Artificial Intelligence (AI), Mathematical Modelling, Autonomous Ships, Ship Manoeuvring, Disturbance Compensation

REFERENCES

Ma G., Li D., Wei Q., Song L. Neural Unilateral Nussbaum Gain Sliding Mode Control for Uncertain Ship Course Keeping with an Unknown Control Direction // Journal of Marine Science and Engineering, 2025. Vol. 13, No. 5. P. 846. - doi:10.3390/jmse13050846

Wang Y., Johansen T.A., Fossen T.I. Artificial Intelligence Challenges for Autonomous Maritime Systems under Uncertain Environmental Conditions // Journal of Marine Science and Technology, 2026. Vol. 31, No. 1. P. 55–71.

Pham, D.-A., & Han, S.-H. (2023). Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System. Journal of Marine Science and Engineering, 11(7), 1262, pp. 1–24. - doi:10.3390/jmse11071262

Zheng, M.; Chen, Q.; Yan, D.; Zhang, Y. (2025). A Comprehensive Review of Ship Automatic Rudder Technology: Development, Challenges, and Prospects. Journal of Marine Science and Engineering, 13(5), 851. - doi:10.3390/jmse13050851

Huang, C., Zhao, Y., Li, B., Zhang, G. (2024). Manual Steering and Robust Adaptive Constrained Control of Ships. Ocean Engineering, 312, Article 119248, pp. 1–19. - doi:10.1016/j.oceaneng.2024.119248

Bertilsson, T. (2022). Adaptive Ship Stabilization: Control of Ship Stabilizing Actuators Using Modified Deep Model Reference Adaptive Control. Master’s Thesis No. 2022:28, Chalmers University of Technology, Department of Mechanics and Maritime Sciences, Gothenburg, Sweden, pp. 1–88. https://hdl.handle.net/20.500.12380/304953

Abudu, R., Bridgelall, R. (2024). Autonomous Ships: A Thematic Review. World, 5(2), pp. 276–292. - doi:10.3390/world5020015

Sutton, R.; Taylor, S. D. H.; Roberts, G. N. (1996). Neuro - Fuzzy Techniques Applied to a Ship Autopilot Design. Journal of Navigation, 49(3), pp. 410–430. - doi:10.1017/S037346330001362X

Velagic, J.; Vukic, Z.; Omerdic, E. (2003). Adaptive Fuzzy Ship Autopilot for Track-Keeping. Control Engineering Practice, 11(4), pp. 433–443. - doi:10.1016/S0967-0661(02)00009-6

Namazi Rabati, H.; Perera, L.P.C. (2025). Adaptive Neuro - Fuzzy Inference for Predicting Control Inputs in Maritime Autonomous Surface Ships Maneuvers. In: Innovations in Sustainable Maritime Technology (IMAM 2025), Springer, pp. 234–244. - doi:10.1007/978-3-032-02102-1_18

Nayak, N.; Das, S.R.; Panigrahi, T.K.; et al. (2023). Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle. Mathematics, 11(8), 1868, pp. 1–18. - doi:10.3390/math11081868

Wang, P.; Pan, M.; Liu, Z.; et al. (2024). Ship Trajectory Prediction in Complex Waterways Based on Transformer and Social Variational Autoencoder (SocialVAE). Journal of Marine Science and Engineering, 12(12), 2233. - doi:10.3390/jmse12122233

Zhang, X.; Chen, Y.; Liu, H. (2024). A CNNGRU-MHA method for ship trajectory prediction based on marine fusion data. Ocean Engineering, 310, 118701. - doi:10.1016/j.oceaneng.2024.118701

Wu, X.; Chen, J.; Xiong, C.; Liu, D.; et al. (2024). Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model. Promet – Traffic & Transportation, 36(6), 1160–1175. - doi:10.7307/ptt.v36i6.772

Liu, W.; Cao, Y.; Guan, M.; Liu, L. (2024). Research on Ship Trajectory Prediction Method Based on CNN-RGRU-Attention Fusion Model. IEEE Access, 12, 63950–63957. - doi:10.1109/ACCESS.2024.3396475

Spadon, G.; Matwin, S.; Pelot, R. (2023). Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion. arXiv preprint. https://arxiv.org/abs/2310.18948 - doi:10.1016/j.oceaneng.2024.119138

Bi, J.; Cheng, H.; Zhang, W.; et al. (2024). Artificial Intelligence in Ship Trajectory Prediction. Journal of Marine Science and Engineering, 12(5), 769, pp. 1–26. - doi:10.3390/jmse12050769

Omerdic, E.; Roberts, G. N.; Vukic, Z. (2003). A fuzzy track-keeping autopilot for ship steering. Journal of Marine Engineering & Technology, 2(1), pp. 23–35. - doi:10.1080/20464177.2003.11020163

Li, H.; Xing, W.; Yang, Z.; et al. (2024). Deep Bi-Directional Information-Empowered Ship Trajectory Prediction for Maritime Autonomous Surface Ships. Transportation Research Part E, 181, 103770. - doi:10.1016/j.tre.2024.103770

Guo, S.; Ma, W. (2026). A Systematic Review of Deep Learning-Based Methods for Ship Trajectory Prediction. Journal of Marine Science and Engineering, 14(9), 810, pp. 1–28. - doi:10.3390/jmse14090810

Nomoto, K.; Taguchi, T.; Honda, K.; Hirano, S. (1957). On the Steering Qualities of Ships. International Shipbuilding Progress, 4(35), pp. 354–370. - doi:10.3233/ISP-1957-43504

Lu, S.; Wang, B.; Dong, Z.; et al. (2025). Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter. Applied Sciences, 15(1), 161, pp. 1–18. - doi:10.3390/app15010161

Bhattacharyya, R. (1978). Dynamics of Marine Vehicles. New York, NY: John Wiley & Sons, Inc., Ocean Engineering Series, pp. x–498. ISBN-13: 978-0-471-07206-5.

Fossen T. I. Handbook of Marine Craft Hydrodynamics and Motion Control. — 2nd ed. — Hoboken, NJ : Wiley, 2021. 710 p. - doi:10.1002/9781119575016

Citation note:

Mykhailenko V., Hvozdeva I., Shevchenko V., Grama G., Leshchenko V.: Intelligent Vessel Course Stabilization Control System. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 20, No. 3, doi:10.12716/1001.20.03.01, pp. 529-538, 2026

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