Artificial Neural Network Controller for Automatic Ship Berthing Using Separate Route
The operation of ships in the port area requires not only the assistance of in-vessel equipment such as main engines and rudder, but also the assistance of external equipment such as tugboats. The complexity in the operation of ships in the port, requires control algorithm with multiple input and output for the automatic berthing control of the ship. The entering and leaving data of the ship can help the algorithm to efficiently control the berthing and un-berthing process of ships. This is based on the artificial intelligence which has been continuously approaching the operating habits of the pilot. The advances in artificial intelligence can control the entering, turning, and berthing in the port by artificial intelligence. In this study, the artificial neural network algorithm has been used to establish an automatic berthing model, based on the scheduled route. With the help of training data of one port, this model can be applied to the ship’s berthing with different berth layouts. Furthermore, it can also be applied to complex systems such as direct or turning-berthing of a ship. Finally, the automatic berthing model has been used for the simulation of direct berthing and turning-berthing in different berth.
L. Trybus, Z. Wider, A. Stec, Tuning rules of conventional and advanced ship autopilot controllers, Advances in Intelligent Systems and Computing, 350(1), pp. 303-311, 2015, http://doi.org/10.1007/978-3-319-15796-2_31.
Zwierzewicz, Zenon, The design of ship autopilot by applying observer based feedback linearization, Polish Maritime Research, 22(1), pp. 16-21, 2015, https://doi.org/10.1515/pomr-2015-0003.
J.Y. Park, N. Kim, Design of an adaptive backstepping controller for auto-berthing a cruise ship under wind loads, International Journal of Naval Architecture & Ocean Engineering, 6(2), pp. 347-360, 2014, http://dx.doi.org/10.2478/ijnaoe-2013-0184.
N.K. Im, V.S. Nguyen, Artificial neural network controller for automatic ship berthing using head-up coordinate system, International Journal of Naval Archi-tecture & Ocean Engineering, 10(3), pp. 235-249, 2018, http://dx.doi.org/10.1016/j.ijnaoe.2017.08.003.
Van-Suong Nguyen, Van-Cuong Do, and Nam-Kyun Im, Development of Auto-matic Ship Berthing System Using Artificial Neural Network and Distance Mea-surement System, International Journal of Fuzzy Logic & Intelligence system, 18(1), pp. 41-49, 2018, http://dx.doi.org/10.5391/IJFIS.2018.18.1.1.
V.L. TRAN, N.K. IM, A Study on Ship Automatic Berthing with Assistance of Auxiliary Devices, International Journal of Naval Architecture and Ocean Engi-neering, 4(3), pp. 199-210, 2012, http://dx.doi.org/10.2478/ijnaoe-2013-0090.
P.H. NGUYEN, Y.C. JUNG, Automatic Berthing Control of Ship Using Adaptive Neural Networks, International Journal of Navigation and Port Research, 31(7), pp. 563-568, 2007, http://dx.doi.org/10.5394/KINPR.2007.31.7.563.
Yaseen Adnan Ahmed, Kazuhiko Hasegawa, Automatic Ship Berthing using Ar-tificial Neural Network Based on Virtual Window Concept in Wind Condition, IFAC Proceedings Volumes, 45(24), pp. 286-291, 2012, http://dx.doi.org/10.3182/20120912-3-BG-2031.00059.
Yaseen Adnan Ahmed, Kazuhiko Hasegawa, Implementation of Automatic Ship Berthing using Artificial Neural Network for Free Running Experiment. IFAC Proceedings Volumes, 46(33), pp. 25-30, 2013, http://dx.doi.org/10.3182/20130918-4-JP-3022.00036.
Yaseen Adnan Ahmed, Kazuhiko Hasegawa, Automatic ship berthing using artifi-cial neural network trained by consistent teaching data using nonlinear program-ming method, Engineering Applications of Artificial Intelligence, 26(10), pp. 2287-2304, 2013, http://dx.doi.org/10.1016/j.engappai.2013.08.009.
Yaseen Adnan Ahmed, Kazuhiko Hasegawa, Experiment Results for Automatic Ship Berthing using Artificial Neural Network Based Controller, IFAC Proceedings Volumes, 47(3), pp. 2658-2663, 2014, http://dx.doi.org/10.3182/20140824-6-ZA-1003.00538.
G.Q. Zhang, X.K. Zhang, Y.F. Zheng, Adaptive neural path-following control for under actuated ships in fields of marine practice, Ocean Engineering, 104(1), pp. 558-567, 2015, http://dx.doi.org/10.1016/j.oceaneng.2015.05.013.
X.K. Zhang, G.G. Zhang, Design of ship course-keeping autopilot using a sine function based nonlinear feedback technique, Journal of Navigation, 69(2), pp. 246-256, 2016, https://doi.org/10.1017/S0373463315000612.
ZHANG Qiang, ZHANG Xianku, IM NK, Ship Nonlinear-Feedback Course Keeping Algorithm Based on MMG Model Driven by Bipolar Sigmoid Function for Berthing, International Journal of Naval Architecture and Ocean Engineering, 9(5), pp. 525-536, 2017, http://dx.doi.org/10.1016/j.ijnaoe.2017.01.004.
H. Yasukawa, Y. Yoshimura, Introduction of MMG standard method for ship maneuvering predictions, Journal of Marine Science & Technology, 20(1), pp. 37-52, 2015, http://dx.doi.org/10.1007/s00773-014-0293-y.