Optimization of an Intelligent Controller for an Unmanned Underwater Vehicle

Amrul Faruq, Shahrum Shah Bin Abdullah, M. Fauzi Nor Shah


 Underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain it position at a certain depth in order to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an unmanned underwater vehicle is described in this paper. The control algorithm is simulated by using the marine guidance navigation and control simulator. The project shows a radial basis function metamodel can be used to tune the scaling factors of a fuzzy logic controller. By using offline optimization approach, a comparison between genetic algorithm and metamodeling has been done to minimize the integral square error between the set point and the measured depth of the underwater vehicle. The results showed that it is possible to obtain a reasonably good error using metamodeling approach in much a shorter time compared to the genetic algorithm approach.

Full Text:



. J. Yuh, R. L., An Intelligent Control System for Remotely Operated Vehicles. IEEE Journal of Oceanic Engineering. 1993. 18(1) : 55-62.

. Ridao, P., Tiano, A., El-Fakdi, Carreras, Zirilli. On the Identification of non-linear models of Unmanned Underwater Vehicle. Control Engineering Practice. 2004. 12 : 1483-1499.

. Budiono, A., Kartidjo, M., Sugama, A., Coefficient Diagram Method for the Control of An Unmanned Underwater Vehicle. Indian Journal of Marine Science. 2009. 38(3): 316-323.

. Santhakumar, M., Asokan, T., A Self-Tuning Proportional-Integral-Derivative Controller for An Autonomous Underwater Vehicle, Based on Taguchi Method. Journal of Computer Science. 2010. 6 (8): 862-871.

. Zanoli, S. M., Conte, G., Remotely Operated Vehicle Depth Control. Control Engineering Practice. 2003. 11 : 453-459.

. Kashif, S. S. Abdullah., Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle. Journal of Intelligent and Robotic Systems. 2010. 59(1): 87-100.

. Smith, S. M., Rae, G.J.S., Anderson, D.T., Application of Fuzzy Logic to the Control of an Autonomous Underwater Vehicle. IEEE International Conference. 1993. 2 : 1099 – 1106.

. Chang, W. J., Chang, W., Liu, H., Model-Based Fuzzy Modelling and Control For Autonomous Vehicle in the Horizontal Plane. Journal of Marine Science and Technology. 2003. 11 (3) : 155-163.

. Healey, J., Lienard, D., Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles. IEEE J. Oceanic Eng. 1993. 18 (3).

. Euan, W. M., Murray, D.J., Li, Y., Fossen, T.I., Ship Steering Control System Optimisation Using Genetic Algorithm. Control Engineering Practice. 1996. 8(2000) : 429-443.

. Kodogiannis, V. S., Lisboa, P.J.G., Lucas, J., Neural Network Modelling and Control for Underwater Vehicles. Artificial Intelligent and Engineering. 1996. 1 : 203-212.

. Chuhran, C. D., Obstacle Avoidance Control For The Remus Autonomous Underwater Vehicle. PhD theses. California, Naval Postgraduate School Monterey. 2003.

. Society of Naval Architects and Marine Engineers (SNAME), Nomenclature for treating the motion of a submerged body through a fluid. 1950. Bull. 1–5.

. Fossen, T. I., Guidance and Control of Ocean Vehicles. England, John Wiley and Sons Ltd. 1994.

. Kleijnen, J. P. C., Statistical tools for simulation practitioners. New York, Marcel Dekker. 1987.

. Simpson, T.W., Peplinski, J., Koch, P.N., Allen, J.K., On the use of statistics in design and the implications for deterministic computer experiments. In: Proc. Design Theory and Methodology (DTM ’97) Sacramento, ASME-DETC97/DTM-3881. Sacramento. 1997.

. Mohamed Ali, M. S., S. S. Abdullah., Controller Optimization for a Fluid Mixing System Using Metamodeling Approach, Int J Simul Model. 2009; 8 (1) : 48-59.

. Ham, F. M., and I. Kostanic, Principles of Neurocomputing for Science and Engineering. Singapore, McGraw-Hill. 2001.

. Mullur, A. M., Extended Radial Basis Functions: More Flexible and Effective Metamodeling. AIAA Journal. 2005; 43(6): 1306-1315.

DOI: http://dx.doi.org/10.12928/telkomnika.v9i2.695

Article Metrics

Abstract view : 149 times
PDF - 124 times


  • There are currently no refbacks.

Copyright (c) 2014 Universitas Ahmad Dahlan

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.