Joint fuzzy controller and fuzzy disturbance compensator in ship autopilot system: investigate stability in environmental conditions


  • Xuan-Kien Dang Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam
  • Le Anh-Hoang Ho Van Hien University, Ho Chi Minh City, Vietnam


disturbance compensator, fuzzy controller, nonlinear system, ship autopilot, tracking control


The autopilot system is a nonlinear and complex control process that results primarily from changes due to the influence of the operating environment.  Separate oceans have different characteristics.  In this paper, we proposed a joint fuzzy controller and fuzzy disturbance compensator (FC-FDC) in the ship autopilot system under the impact of the nonlinear error caused by feedback states errors, random delays, uncertain models, and environmental disturbances.  In the process of controlling, the designed structure of FC-FDC not only performs ship tracking control but also reduces the effect disturbance while increasing the robustness of the system.  As exemplified by selecting types of ships for simulating the FC-FDC ship autopilot system in MATLAB, the simulation results proved the efficiency and advantages of the proposed method.


Dang, X. K., Ho, L. A. H., & Do, V. D. (2018). Analyzing the sea weather effects to the ship maneuvering in Vietnam’s Sea from Binh Thuan province to Ca Mau province based on fuzzy control method. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 16(2), 533-543. DOI: 10.12928/telkomnika.v16i2.7753

Dang, X. K., Nguyen, T. Q., & Nguyen, X. P. (2015). Ship autopilot system design and testing on Santana ship model based on neural-fuzzy method. In Proc. 3rd Vietnam Conference on Control and Automation – VCCA, 683-689. DOI: 10.15625/vap.2015.0016

Das, S., & Talole, S. E. (2016). Robust steering autopilot design for marine surface vessels. IEEE Journal of Oceanic Engineering, 41(4), 913-922. DOI: 10.1109/JOE.2016.2518256

Deng, W., Xu, J., & Zhao, H. (2019). An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access, 7, 20281-20292. DOI: 10.1109/ACCESS.2019.2897580

Do, V. D., & Dang, X. K. (2019). The fuzzy particle swarm optimization algorithm design for dynamic positioning system under unexpected impacts. Journal of Mechanical Engineering and Sciences (JMES), 13(3), 5407-5423. DOI: 10.15282/jmes.13.3.2019.13.0439

Do, V. D., Dang, X. K., Huynh, L. M. T., & Ho, V. C. (2019). Optimized multi-cascade fuzzy model for ship dynamic positioning system based on genetic algorithm. International Conference on Industrial Networks and Intelligent Systems, 293, 165-180. DOI: 10.1007/978-3-030-30149-1_14

Ejaz, M., & Chen, M. (2017). Optimal backstepping control for a ship using firefly optimization algorithm and disturbance observer. Transactions of the Institute of Measurement and Control, 40(6), 1983-1998. DOI: 10.1177/0142331217695388

Fang, M. C., Zhuo, Y. Z., & Lee, Z. Y. (2010). The application of the self-tuning neural network PID controller on the ship roll reduction in random waves. Ocean Engineering, 37(7), 529-538. DOI: 10.1016/j.oceaneng.2010.02.013

Fossen, T. I. (2000). A survey on nonlinear ship control: from theory to practice. IFAC Conference on Manoeuvring and Control of Marine Craft, 33(21), 1-16. DOI: 10.1016/S1474-6670(17)37044-1

Hu, X., Du, J., & Shi, J. (2015). Adaptive fuzzy controller design for dynamic positioning system of vessels. Applied Ocean Research, 53, 46-53. DOI: 10.1016/j.apor.2015.07.005

Ishaque, K., Abdullah, S. S., Ayob, S. M., & Salam, Z. (2011). A simplified approach to design fuzzy logic controller for an underwater vehicle. Ocean Engineering, 38(1), 271-284. DOI: 10.1016/j.oceaneng.2010.10.017

Li, Z., & Sun, J. (2012). Disturbance compensating model predictive control with application to ship heading control. IEEE Transactions on Control Systems Technology, 20(1), 257-265. DOI: 10.1109/TCST.2011.2106212

Liu, Z., Lu, X., & Gao, D. (2019). Ship heading control with speed keeping via a nonlinear disturbance observer. Journal of Navigation, 72(4), 1035-1052. DOI: 10.1017/S0373463318001078

McGookin, E. W., Smith, D. J. M., Li, Y., & Fossen. T. I. (2000). The optimization of a tanker autopilot control system using genetic algorithms. Transactions of the Institute of Measurement and Control, 22(2), 141-178. DOI: 10.1177/014233120002200203

Moradi, M. H., & Katebi, M. R. (2001). Predictive PID control for ship autopilot design. IFAC Proceedings, 34(7), 375-380. DOI: 10.1016/S1474-6670(17)35111-X

Narwane, V. S., Narkhede, B. E., Bhosale, V. V., & Jain, P. (2020). Comparative analysis of PID and fuzzy logic controller: A case of furnace temperature control. Journal of Current Science and Technology, 10(2), 109-120. DOI: 10.14456/jcst.2020.11

Sheng, L., Ping, Y., Yan, L. Y., & Chun, D. Y. (2006). Application of H infinite control to ship steering system. Journal of Marine Science and Application, 5(1), 6-11. DOI: 10.1007/s11804-006-0041-8

Sutton, R., Roberts, G. N., & Dearden, S. R. (1989). Design study of a fuzzy controller for ship roll stabilization. Electronics & Communication Engineering Journal, 1(4), 159-166. DOI: 10.1049/ecej:19890033

Ta, V. P., Dang, X. K., Dong, V. H., & Do, V. D. (2018). Designing dynamic positioning system based on H∞ robust recurrent cerebellar model articulation controller. 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), 652-657. DOI: 10.1109/GTSD.2018.8595553

Tomera, M. (2014). Ant colony optimization algorithm applied to ship steering control. Procedia Computer Science, 35, 83-92. DOI: 10.1016/j.procs.2014.08.087

Yang, Z., Wang, J. H., & Wu, Y. P. (2014). Straight line path following of unmanned surface vessel based on Fuzzy PID. Computer Engineering, 40(10), 270-274.

Zhang, Q., Jiang, N., Hu, Y., & Pan, D. (2017). Design of course-keeping controller for a ship based on backstepping and neural networks. International Journal of e-Navigation and Maritime Economy, 7, 34-41. DOI: 10.1016/j.enavi.2017.06.00




How to Cite

Xuan-Kien Dang, & Le Anh-Hoang Ho. (2023). Joint fuzzy controller and fuzzy disturbance compensator in ship autopilot system: investigate stability in environmental conditions. Journal of Current Science and Technology, 11(1), 114–126. Retrieved from



Research Article