Robustness Analysis of GWO/PID Approach in Control of Ball Hoop System with ITAE Objective Function
DOI:
https://doi.org/10.26438/ijcse/v6i8.218222Keywords:
BH System, GWO, ITAE, Robustness, PIDAbstract
This work deals with robustness analysis of Grey Wolf Optimization (GWO) / Proportional-Integral-Derivative (PID) approach in control of ball hoop (BH) system with integral of time multiplied absolute error (ITAE) objective function. The robustness analysis of GWO/PID approach has been carried out with ±5% perturbation in the locations of the poles of the BH system. It has been observed that proposed GWO/PID approach with ITAE objective function gives satisfactory performance with ±5% perturbation.
References
[1] J. G. Ziegler, Ν. B. Nichols, "Optimum setting for automatic controllers", Trans. ASME, Vol. 64, pp. 7759-7768, 1942.
[2] G.H. Cohen and G.A. Coon, “Theoretical Investigation of Retarded Control”, The American Society of Mechanical Engineer, Vol. 75, pp. 827-834, 1953.
[3] M. Zhuang and D.P. Atherton , “Automatic tuning of optimum PID controllers”, IEEE Proceedings, Vol. 140, Issue 3, pp. 216-224, 1993.
[4] K.J. Astrom and T. Hagglund, “The future of PID control”, Control Engineering Practice, pp. 1163- 1175, 2001.
[5] 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.
[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, volume 7, Issue 2, pp. 49-54, 2007.
[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] 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] 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] 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, Taylor & Francis, Vol. 5, pp. 1-18, 2018.
[14] S. Mirjalili, S. M. Mirjalili and A. Lewis, “Grey Wolf Optimizer”, Adv. Eng. Softw.,ELSEVIER, Vol. 69, pp. 46-61, 2014.
[15] 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].
[16] C. Muro, R. Escobedo, L. Spectoe and R. P. Coppinger, “Wolf-pack (Canis Lupus) hunting strategies emerge from simple rules in computational simulations”, ELSEVIER, Vol. 88, pp. 192-197, 2011.
[17] P. Wellstead, “ The ball and hoop system”, Automatica,Vol.19, No. 4, pp. 401-406,1983.
[18] G. Parmar, S. Mukharjee and R. Prasad, “Reduced order modelling of linear multivariable system using particle swarm optimization technique”, International Journal of Innovative Computing and Applications, Vol.1, No. 2, pp. 128–137, 2007.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
