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Model Identification of an Underwater Remotely Operated Vehicle Using System Identification Approach Based on NNPC

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This paper described the development of modeling of an unmanned underwater vehicle (UUV) using system identification toolbox based on neural network model. The set of data based on neural network model generated by open-loop model of UUV and the input-output data produced using neural network predictive control technique. The model of UUV is an underwater Remotely Operated Vehicle (ROV) will be used in this study. Open-loop model of ROV created using system identification technique with implemented in real time experiment for open-loop system. Two data will be used such as the input and output neural network data for validation and training for infer a model of the ROV using system identification toolbox. The data re-generated using graph digitizer software. The accuracy of this software almost 90%. Then, the model obtained in this system will be controlled using conventional PID controller in MATLAB Simulink. The comparison between two models from different techniques of the ROV will be described. When the number of samples used in this project reduced, the best fit will be increased. A model obtained based on neural network model is acceptable to use in simulation and will be improved the best fit when reduced number of samples.
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Neural Network Predictive Control; Neural Network Model; Graph Digitizer; System Identification

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F.A.Azis, M.S.M. Aras, S.S. Abdullah, Rashid, M.Z.A, M.N. Othman. Problem Identification for Underwater Remotely Operated Vehicle (ROV): A Case Study. Procedia Engineering. 2012; 41: 554-560.

M. S. M. Aras, F.A.Azis, M.N.Othman, S.S.Abdullah. A Low Cost 4 DOF Remotely Operated Underwater Vehicle Integrated With IMU and Pressure Sensor. In: 4th International Conference on Underwater System Technology: Theory and Applications 2012 (USYS'12), pp 18-23, 2012.

A.S.MohdNor, S.S.Abdullah, M.S.M.Aras, M.Z.A Rashid, Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV), 4th International Conference on Underwater System Technology: Theory and Applications 2012 (USYS'12), 5th & 6th December, pp 24-29, 2012.

Ali, F.A., Azis, F.A., Aras, M.S.M., Othman, M.N., Abdullah, S.S., Design of a magnetic contactless thruster of Unmanned Underwater Vehicle, (2013) International Review of Mechanical Engineering (IREME), 7 (7), pp. 1413-1420.

Aras, M.S.M, S.S. Abdullah, Rashid, M.Z.A, Rahman, A. Ab,Aziz, M.A.A, Development and Modeling of underwater Remotely Operated Vehicle using System Identification for depth control, Journal of Theoretical and Applied Information Technology, Vol. 56 No 1, pp 136-145, 2013.

MohdShahrieelMohd Aras, Shahrum Shah Abdullah, Azhan Ab Rahman, Muhammad AzharAbd Aziz, Thruster Modelling for Underwater Vehicle Using System Identification Method, International Journal of Advanced Robotic Systems, Vol. 10, No 252, pp 1 – 12, 2013.

Mohd Aras, MohdShahrieel and Abdullah, Shahrum Shah and Shafei, SitiSaodah, Investigation and Evaluation of Low cost Depth Sensor System Using Pressure Sensor for Unmanned Underwater Vehicle. Majlesi Journal of Electrical Engineering, Vol. 6, (No. 2), 2012.

Mohd Aras, M.S., Abdullah, S.S., Abd Aziz, M.A., Rahman, A.F.N.A., Analysis of an improved Single Input Fuzzy Logic Controller designed for depth control using Microbox 2000/2000c interfacing, (2013) International Review of Automatic Control (IREACO), 6 (6), pp. 728-733.

Aras, M.S.M, S.S. Abdullah, Rashid, M.Z.A, Rahman, A. Ab, Aziz, M.A.A, Robust Control of Adaptive Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle, Journal of Theoretical and Applied Information Technology, Vol. 57, 2013.

Mohd Aras, MohdShahrieel and MohdFarriz , MdBasar and Abdul Azis, Fadilah and FaraAshikin , Ali,Analysis Movement of Unmanned Underwater Vehicle using the Inertial Measurement Unit. International Journal of Emerging Science and Engineering (IJESE), 1 (10). pp. 47-53. ISSN 2319–6378, 2013.

Mohd Aras, MohdShahrieel and MdBasar, MohdFarriz and AbdAzis, Fadilah and Ali, FaraAshikin, Obstacle Avoidance System for Unmanned Underwater Vehicle using Fin System. International Journal of Science and Modern Engineering (IJISME), 1 (9). pp. 24-30. ISSN 2319-6386, 2013.

Jebelli, A., Yagoub, M.C.E., Abdul Rahim, R.H.J., Kazemi, H., Design and construction of an underwater robot based fuzzy logic controller, (2013) International Review of Mechanical Engineering (IREME), 7 (1), pp. 147-153.

Mashhad, A.M., Karsaz, A., Mashhadi, S.K.M., High maneuvering multiple-underwater robot tracking with optimal two-stage kalman filter and competitive hopfield neural network based data fusion, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (4), pp. 191-198.

Aras, M.S.M., Abdullah, S.S., Jaafar, H.I., Razilah, A.R., Arfah Ahmad, A., A comparison study between two algorithms particle swarm optimization for depth control of underwater remotely operated vehicle, (2013) International Review on Modelling and Simulations (IREMOS), 6 (5), pp. 1687-1694.


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