A Comparison Study between Two Algorithms Particle Swarm Optimization for Depth Control of Underwater Remotely Operated Vehicle

Mohd Shahrieel b Mohd Aras(1*), Shahrum Shah b Abdullah(2), Hazriq Izzuan Jaafar(3), Razilah Abdul Rahim(4), Arfah Ahmad(5)

(1) Universiti Teknologi Malaysia (UTM), Malaysia
(2) Universiti Teknologi Malaysia (UTM), Malaysia
(3) Universiti Teknologi Malaysia (UTM), Malaysia
(4) Universiti Teknikal Malaysia Melaka UTeM, Malaysia
(5) Universiti Teknikal Malaysia Melaka UTeM, Malaysia
(*) Corresponding author

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


This paper investigates two algorithms based on particle swarm optimization (PSO) to obtain optimum parameter. In this research, an improved PSO algorithm using a priority-based fitness PSO (PFPSO) and priority-based fitness binary PSO (PFBPSO) approach. This comparison study between two algorithms applied on underwater Remotely Operated Vehicle for depth control.  Two parameters in Single Input Fuzzy Logic Controller will tune using two algorithms to obtain optimum parameter. There are two parameters to be tuned namely the break point and slope for the piecewise linear or slope for the linear approximation. The study also covered a comparison for time execution for every time the parameter tuning was done. Based on the results the PFBPSO gives a consistent value of optimum parameter and time execution very fast. The best optimum parameter of SIFLC determined using 2 methods such that average of optimum parameter and intersection of y-axis. The PFBPSO gives comparative results in term of two parameters and time execution very fast compared with improved PSO.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Priority Fitness PSO; Priority Fitness Binary PSO; Optimum Parameter; Single Input FLC; Time Execution

Full Text:



Kennedy, J. and Eberhart, R. “Particle Swarm Optimization”, Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.

J. Kennedy and R. Eberhart, A discrete binary version of the particle swarm algorithm, Proc. Of IEEE International Conference on Systems, Man, and Cybernetics, pp.4104-4108, 1997.

M. I. Solihin, Wahyudi, M.A.S Kamal and A. Legowo, Optimal PID Controller Tuning Of Automatic Gantry Crane Using PSO Algorithm,” Proceeding of the 5th International Symposium on Mechatronics and its Applications (ISMA08), Amman, Jordan, May 27-29, pp. 1-5, 2008.

Clerc, M.; Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 1, (2002) 58-73.

Pitono, J., Soeprijanto, A., Purnomo, M.H., Gunadin, I.C., Power generation optimization based on steady state stability limit using particle swarm optimization (PSO), (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1227-1232.

Tae-Hyoung Kim, Ichiro Maruta and Toshiharu Sugie, 2007 “Robust PID Controller Tuning Based on Constrained Particle Swarm Optimization”, Automatica, 44(4), pp. 1104-1110, 2008

Hazriq Izzuan Jaafar, Z. Mohamed, Amar Faiz Zainal Abidin, Z. Ab Ghani, PSO-Tuned PID Controller for a Nonlinear Gantry, Crane System, IEEE International Conference on Control System, Computing and Engineering, 23 - 25 Nov. 2012, pp 1-5.

Hazriq Izzuan Jaafar, Nursabillilah Mohd Ali, Z. Mohamed, Nur Asmiza Selamat, Anuar Mohamed Kassim, Amar Faiz Zainal Abidin, J.J. Jamian, Optimal Performance of a Nonlinear Gantry Crane System via Priority-based Fitness Scheme in Binary PSO Algorithm,pp 1 -6, 2013.

Alimi, S., Chtourou, M., Stability analysis of fuzzy dynamic model identification, (2012) International Review on Modelling and Simulations (IREMOS), 5 (1), pp. 506-516.

M.S.M Aras, S.S. Abdullah, H.I. Jaafar, A. A Rahman, M.A.A Aziz, Single Input Fuzzy Logic Controller tuning using PSO based on Simple Feed Forward and Output Feedback Observer for Underwater Remotely Operated Vehicle, Submitted to related journal (under review), 2013.

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; Volume 41, pp: 554-560, 2012.

Mohd Shahrieel Mohd Aras, Shahrum Shah Abdullah, Azhan Ab Rahman, Muhammad Azhar Abd Aziz, Thruster Modelling for Underwater Vehicle Using System Identification Method, International Journal of Advanced Robotic Systems, Vol. 10, pp 1 – 12, 2013.

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), 2012 Malaysia, pp 18-23.

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,Jatit, 2013.

E. J. Solteiro Pires, J. A. Tenreiro Machado and P. B. de Moura Oliveira, Particle Swarm Optimization: Dynamical Analysis through Fractional Calculus, Chapter 24, InTech Publisher, 2009.

J. Kennedy and R. Eberhart, A discrete binary version of the particle swarm algorithm, Proc. of IEEE International Conference on Systems, Man, and Cybernetics, pp.4104-4108, 1997.

Kassim, A.M., Yasuno, T., Abas, N., Aras, M.S.M., Rashid, M.Z.A., Performance study of reference height control algorithm for tripod hopping robot, (2013) International Review of Mechanical Engineering (IREME), 7 (5), pp. 784-789.

Panda, M.K., Pillai, G.N., Kumar, V., Power system stabilizer design using interval type-2 fuzzy logic control, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6252-6265.

De Melo, L.F., Alves, S.F.R., Rosário, J.M., Mobile Robot navigation modelling, control and applications, (2012) International Review on Modelling and Simulations (IREMOS), 5 (2), pp. 1059-1068.


  • There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize