International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Artificial neural network controller for automatic ship berthing using head-up coordinate system

  • Im, Nam-Kyun (Department of Navigation Science, Mokpo National Maritime University) ;
  • Nguyen, Van-Suong (Faculty of Navigation, Vietnam Maritime University)
  • Received : 2016.04.04
  • Accepted : 2017.08.01
  • Published : 2018.05.31
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Abstract

The Artificial Neural Network (ANN) model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports.

Keywords

References

  1. Ahmed, Y.A., Hasegawa, K., 2013. Automatic ship berthing using artificial neural network trained by consistent teaching data using non-linear pro-gramming method. J. Eng. Appl. Artif. Intell. 26 (10), 2287-2304. https://doi.org/10.1016/j.engappai.2013.08.009
  2. Im, N.K., 2009. All direction approach automatic ship berthing controller using ANN. J. Control Autom. Syst. Eng. 13, 123-129.
  3. Im, N.K., Hasegawa, K., 2001. A study on automatic ship berthing using parallel neural controller. J. Kansai Soc. Nav. Archit. Jpn 236, 65-70.
  4. Im, N.K., et al., 2007. An application of ANN to automatic ship berthing using selective controller. J. Mar. Navig Saf. Sea Transp. 1, 101-105.
  5. Kijima, K., et al., 1990. On the maneuvering performance of a ship with the parameter of loading condition. J. Soc. Nav. Archit. Jpn. 168 (2), 141-148.
  6. Nguyen, P.H., et al., 2007. Automatic berthing control of ship using adaptive neural network. In: International Symposium on Electrical and Electronics Engineering. HCM City, Vietnam.
  7. Park, J.Y., Kim, N., 2014. Design of an adaptive backstepping controller for auto-berthing a cruise ship under wind loads. Int. J. Nav. Archit. Ocean Eng. 6 (2), 347-360. https://doi.org/10.2478/IJNAOE-2013-0184
  8. Tran, V.L., Im, N.K., 2012. A study on automatic berthing with assistance of auxiliary devices. Int. J. Nav. Archit. Ocean Eng. 4 (3), 199-210. https://doi.org/10.2478/IJNAOE-2013-0090
  9. Yamato, H., et al., 1990. Automatic berthing by the neural controller. In: Proceeding of Ninth Ship Control Systems Symposium, vol. 3, pp. 3.183-3.201.
  10. Yamato, H., et al., 1992. Automatic berthing using expert system. In: Proceeding of Workshop on Artificial Intelligence Control and Advanced Technology in Marine Automation (CAMS'92), pp. 173-183.
  11. Zhang, Y., et al., 1997. Neural network approaches to a class of ship control problems (part I, II). In: Wilson, P.A. (Ed.), Eleventh Ship Control Systems Symposium, vol. 1, pp. 115-150.

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