International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-12, October 2019 5714 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: L40031081219/2019©BEIESP DOI: 10.35940/ijitee.L4003.1081219 Abstract: An Artificial Neural Network is a well-known AI technique for replicating human brain and offering suitable solution for any unpredictable complicated problem. Taking the advantage of it, this research will analyse the applicability of Neural Network Controller for ship manoeuvring, such as course changing. To train the controller, optimized teaching data are used to keep the consistency in the data as it could enhance the learning ability of the controller while training. A double layered feed-forward neural network and back propagation method are found suitable for this purpose. Later-on, simulations are done to justify the effectiveness of the trained controller for unknown situations. Index Terms: Artificial Neural Network, Intelligent Controller, Numerical Analysis, Optimisation, Ship Manoeuvring I. INTRODUCTION Manoeuvring of a ship greatly depends on the knowledge of an operator that he gains through years of experience. Therefore, an inexperienced captain often faces difficulties in unfavourable situations, such as manoeuvring through narrow channels or navigating under high wind disturbances. In such a situation, relying on the automation would be a great relief for them. However, while using conventional control, a successful application could only be found within a well-constrained environment. As a result, researchers are very keen on developing intelligent controllers, which can deal with the robustness and propose a suitable solution for any given problem. One of such intelligent systems is Artificial Neural network, which is inspired by the central nervous system of human‟s and can replicate the human‟s action in solving complex problems with a lot of uncertainties. Yamato, Uetsuki and Koyama [1] first used ANN as a controller and he chose it for automatic ship berthing. Later on, Fuji and Ura [2] ensured that ANN could be used as both supervised and non-supervised controller. After him, researchers continued to use ANN for temperature control, paper mill waste-water treatment control, process control etc. Hasegawa and Kitera [3] and Im and Hasegawa [4], [5] had continued their research on applying ANN for automatic ship berthing. However, the success was not up to mark as the controller often confused to navigate the ship up to the pier in wind disturbances. By this time, Ohtsu, Mizuno, Kuroda and Okazaki [6] proposed a new strategy to do optimisation using nonlinear programming language (NPL) method which allowed the user to carry out the optimisation Revised Manuscript Received on October 05, 2019. Yaseen Adnan Ahmed, Maritime Engineering Technology Department, Umiversity Kuala Lumpur, Malaysian Institute of Marine Engineering Technology, Lumut, Malaysia. yaseen.ahmed@unikl.edu.my Iwan Zamil Mustaffa Kamal, Maritime Engineering Technology Department, Umiversity Kuala Lumpur, Malaysian Institute of Marine Engineering Technology, Lumut, Malaysia. iwanzamil@unikl.edu.my Mohammad Abdul Hannan, Marine Technology Department, Newcastle University in Singapore, Singapore. for any desired set of equality and non-equality constraints. His strategy helped a number of researchers who were struggling to train the controller effectively as they need to do a lot of experiments to get the data, which were no doubt expensive, but also time-consuming. A paper published by Ahmed and Hasegawa [7], [8] clearly demonstrates how to utilize Ohtsu‟s [6] proposed method in creating consistent teaching data and get a well-trained controller for ship manoeuvring. This research will use the same strategy to create the teaching data, which are consistent and use it to train a neural network for different course changing. Changing heading is a simple manoeuvre, however, to change it within a minimum time is a chal-lenge. Therefore, the research is aimed to propose an ANN controller which is able to change the ship‟s course in minimum time. An optimisation function „fmincon‟ is used for creating teaching data and Levenberg-Marquardt algorithm is used to train the net on Matlab. Several types of networks are then analysed and the one with minimum mean squared error (MSE) is selected for further research. Simulations are also carried out for unknown cases to demonstrate the effectiveness of the proposed controller. II. METHODOLOGY Ship Model As for this research, a S60 container ship has been chosen, which is equipped with a single rudder and a single propeller. The principle particulars of this ship is given in Table I. Table. 1 Ship particulars Parameter Value L [m] 276.0 B [m] 39.40 d [m] 15.80  0.7 Creation of Teaching Data In order to train a neural network controller, a consistent set of teaching data is needed. As the controller is for course changing manoeuver, and there are a number of possible ways to turn a certain degree of course, this research investigates only the minimum time course changing manoeuvre to make sure the similarity in teaching data. To do so, optimisation technique is used on Matlab. Among the different types of optimisation functions available on Matlab optimisation toolbox, „fmincon‟ is found to be the best as it uses the nonlinear programming language (NPL) method [6]. An Artificial Neural Network Controller for Course Changing Manoeuvring Yaseen Adnan Ahmed, Iwan Zamil Mustaffa Kamal, Mohammad Abdul Hannan