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Application of GWO in Control of BH System with ISE Objective Function

Vijay Kumar1 , Girish Parmar2 , Rajesh Bhatt3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 131-136, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.131136

Online published on Jul 31, 2018

Copyright © Vijay Kumar, Girish Parmar, Rajesh Bhatt . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Vijay Kumar, Girish Parmar, Rajesh Bhatt, “Application of GWO in Control of BH System with ISE Objective Function,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.131-136, 2018.

MLA Style Citation: Vijay Kumar, Girish Parmar, Rajesh Bhatt "Application of GWO in Control of BH System with ISE Objective Function." International Journal of Computer Sciences and Engineering 6.7 (2018): 131-136.

APA Style Citation: Vijay Kumar, Girish Parmar, Rajesh Bhatt, (2018). Application of GWO in Control of BH System with ISE Objective Function. International Journal of Computer Sciences and Engineering, 6(7), 131-136.

BibTex Style Citation:
@article{Kumar_2018,
author = {Vijay Kumar, Girish Parmar, Rajesh Bhatt},
title = {Application of GWO in Control of BH System with ISE Objective Function},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {131-136},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2406},
doi = {https://doi.org/10.26438/ijcse/v6i7.131136}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.131136}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2406
TI - Application of GWO in Control of BH System with ISE Objective Function
T2 - International Journal of Computer Sciences and Engineering
AU - Vijay Kumar, Girish Parmar, Rajesh Bhatt
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 131-136
IS - 7
VL - 6
SN - 2347-2693
ER -

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Abstract

This work deals with performance evaluation of integral square error (ISE) objective function in determining the optimal parameters of proportional-integral-derivative (PID) controller for control of ball hoop system using Grey Wolf Optimization (GWO) algorithm. The GWO is recently proposed bio inspired heuristic algorithm inspired by both the social hierarchy and hunting strategy of grey wolves. Comparison of proposed GWO/PID scheme with other existing techniques has also been shown in graphical and tabular forms. It has been observed that proposed GWO/PID approach with ISE as an objective function gives less settling time and overshoot when compared with existing approaches in the literature.

Key-Words / Index Term

Ball Hoop System, PID Controller, Grey Wolf Optimization, Meta-Heuristic, Integral Square Error (ISE)

References

[1] M. Zhuang and D.P. Atherton , “Automatic tuning of optimum PID controllers”, IEEE Proceedings, Vol. 140, Issue 3, pp. 216-224, 1993.
[2] K. J. Astrom and T. Hagglund, “The future of PID control”, Control Engineering Practice, pp. 1163- 1175, 2001.
[3] K.H. Ang, and G. Chong, Y. Li, “PID control system analysis, design and technology”, IEEE Trans. Control System Technology, Vol. 13, pp. 559-576, 2005.
[4] J. G. Ziegler, Ν. B. Nichols, "Optimum setting for automatic controllers", Trans. ASME, Vol. 64, pp. 7759-7768, 1942.
[5] G.H. Cohen and G.A. Coon, “Theoretical Investigation of Retarded Control”, The American Society of Mechanical Engineer, Vol. 75, pp. 827-834, 1953.
[6] O. Roeva and T. Slavov, “PID Controller tuning based on metaheuristic algorithms for bioprocess control”, Biotechnology & Biotechnological Equipment, pp. 3267-3277, 2014.
[7] S. Pareek, M. Kishnani and R. Gupta, “Optimal tuning of PID controller using meta heuristic algorithms”, IEEE International Conference on Advances in Engineering & Technology Research (ICAETR ), 2014.
[8] P. Sreekanth and A. Hari, “Genetic algorithm based self tuning regulator for ball and hoop system”, IEEE Conference on Emerging Devices and Smart Systems (ICEDSS), 2016.
[9] M. EI-Said EI-Telbany, “Employing Particle Swarm Optimizer and Genetic Algorithms for Optimal Tuning of PID Controllers: A Comparative Study”, ICGST-ACSE Journal, Vol. 7, No. 2, pp. 49-54, 2007.
[10] [10] H. Mojallali, R. Gholipour, A. Khosravi and H. Babaee, “ Application of chaotic particle swarm optimization to PID parameter tuning in ball and hoop system”, International Journal of Computer and Electrical Engineering, Vol. 4, No. 4, pp. 452-457, 2012.
[11] [11] Morkos and H. Kamal, “ Optimal tuning of PID controller using adaptive hybrid particle swarm optimization algorithm” , Int. J. of Computers, Communications & Control, Vol VII, No.1, pp. 101-114, 2012.
[12] [12] D. Davendra, I. Zelinka and R. Senkerik, “Chaos driven evolutionary algorithms for the task of PID Control”, Computers & Mathematics with Applications, Vol. 60, No. 4, pp.1088–1104, 2010.
[13] [13] N. Jain, G. Parmar and R. Gupta, “ Performance of PID controller of nonlinear system using swarm intelligence techniques”, ICTACT Journal on Soft Computing,Vol. 6, pp. 1314-1318, 2016.
[14] [14] E. S. Ali and S. M. Abd-Elazim, “Bacteria foraging optimization algorithm based load frequency controller for interconnected power system”, Electric Power and Energy Systems, Vol. 33, pp. 633-638, 2011.
[15] [15] S. Mirjalili, S. M. Mirjalili and A. Lewis, “Grey Wolf Optimizer”, Adv. Eng. Softw., ELSEVIER, Volume 69, pages 46-61, 2014.
[16] [16] V. Soni, G. Parmar and M. Kumar, “A hybrid grey wolf optimisation and pattern search algorithm for automatic generation control of multi area interconnected power system”, Int. J. of Advanced Intelligence Paradigms, Inderscience [In Press].
[17] [17] L.D. Mech, “Alpha status, dominance, and division of labor in wolf packs”, Canadian Journal of Zoology, Volume 77, 1999.
[18] [18] C. Muro, R. Escobedo, L. Spectoe and R. P. Coppinger, “Wolf-pack (Canis Lupus) hunting strategies emerge from simple rules in computational simulations”, ELSEVIER, Volume 88, pages 192-197, 2011.
[19] [19] P. Wellstead, “ The ball and hoop system”, Automatica,Vol. 19, No. 4, pp. 401-406,1983.
[20] [20] Z.-L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Trans. Energy Conversion, Volume 19, pages 384-391, June 2004.
[21] [21] Dervis Karaboga, Bahriye Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm”, Journal of Global Optimization, Vol. 39, No. 3, pp. 459-471, 2007.
[22] [22] Kevin M. Passino, “Biomimicy of Bacterial Foraging for Distributed Optimization and Control”, IEEE Control Systems, Vol. 22, No. 3, pp. 52-67, 2002.
[23] [23] N. Jain, G. Parmar, R. Gupta and I. Khanam, “ Performance evaluation of GWO/PID approach in control of ball hoop system with different objective functions and perturbation”, Cogent Engineering, Vol. 5, pp. 1-18, 2018.