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.

BH System, PID, GWO, ITAE, Robustness

[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.