Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

• Autorzy: Ahmed Y. , Hasegawa K.

Identyfikatory

DOI

10.12716/1001.09.03.15

• Języki publikacji: EN

Abstrakty

EN

In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

Słowa kluczowe

EN

Port Maneuvres; Artificial Neural Network (ANN); Automatic Ship Berthing Control; Ship Berthing; Automatic Ship Berthing; Monte Carlo simulation; Autonomous Underwater Vehicle (AUV); Teaching Data Creation

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2015
• Tom: Vol. 9, no. 3
• Strony: 417--426
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Ahmed Y.

• Osaka University, Osaka, Japan

autor

Hasegawa K.

• Osaka University, Osaka, Japan

Bibliografia

• 1 Ahmed, Y. A. and Hasegawa, K. 2013a. Automatic Ship Berthing using Artificial Neural Network Trained by Consistent Teaching Data using Non‐Linear Programming Method. Journal of Engineering Applications of Artificial Intelligence, vol. 26, issue 10, pp.2287‐2304.
• 2 Ahmed, Y. A. and Hasegawa, K. 2013b. Implementation of Automatic Ship Berthing using Artificial Neural Network for Free Running Experiment. Proc. of the 9th IFAC Conference on Control Applications in Marine Systems, vol. 9, pp.25‐30, Osaka, Japan.
• 3 Endo, M. and Hasegawa, K. 2003. Passage Planning System for Small Inland Vessels Based on Standard Paradigms and Manoeuvres of Experts. MARSIM’03, vol._, pp.RB‐ 19‐1‐RB‐19‐9.
• 4 Fujiwara, T., Ueno, M. and Nimura, T. 1998. Estimation of Wind Forces and Moments acting on Ships. Journal of the Society of Naval Architects of Japan, vol.183, pp.77‐90. (In Japanese)
• 5 Fujii, T. and Ura, T. 1991. Neural‐Network‐Based Adaptive Control Systems for AUVs. Journal of Engineering Applications of Artificial Intelligence, vol. 4, pp.309‐318.
• 6 Hasegawa, K. and Kitera, K. 1993. Automatic Berthing Control System using Network and Knowledge‐base. Journal of Kansai Society of Naval Architects of Japan, vol. 220, pp.135‐143. (in Japanese).
• 7 IM, N.K. and Hasegawa, K. 2001. A Study on Automatic Ship Berthing Using Parallel Neural Controller. Journal of Kansai Society of Naval Architects of Japan, vol. 236, pp. 65‐70.
• 8 IM, N.K. and Hasegawa, K. 2002. A Study on Automatic Ship Berthing Using Parallel Neural Controller (2nd Report). Journal of Kansai Society of Naval Architects of Japan, vol. 237, pp.127‐132.
• 9 Kose, K., Hinata, H., Hashizume, Y. and Futagawa, E. 1986. On a Computer Aided Manoeuvring System in Harbors. Journal of Society of Naval Architects of Japan, vol.160, pp.103‐110. (in Japanese)
• 10 Nakata, M. and Hasegawa, K. 2003. A Study on Automatic Berthing Using Artificial Neural Network‐ Verification of Model Ship Berthing Experiments. Journal of Kansai Society of Naval Architects of Japan, vol. 240, pp. 145‐150.
• 11 Ohtsu, K., Mizuno, N., Kuroda, M. and Okazaki, T. 2007. Minimum Time Ship Manoeuvring Method Using Neural Network and Nonlinear Model predictive Compensator. Journal of Control Engineering Practice, vol. 15, issue 6, pp.757‐765.
• 12 The Specialist Committee on Esso Osaka. Final Report and Recommendations to the 23rd ITTC. Proc. of the 23rd ITTC, vol. 2, pp.573‐609.
• 13 Yamato, H., Uetsuki, H. and Koyama, T. 1990. Automatic Berthing by Neural Controller. Proc. Of Ninth Ship Control Systems Symposium, vol. 3, pp.3.183‐201.