Abstract
The main aim of this chapter is to obtain optimal gains for a PID controller using multi-objective genetic algorithm used in a coupled-tank liquid-level control system. Liquid level control system is a nonlinear system and finds a wide application in petrochemical, food processing, and water treatment industries, and the quality of control directly affects the quality of products and safety. This chapter employs the use of multi-objective genetic algorithm for the optimization of the PID gains for better plant operations in contrast to conventional tuning methods and GA. The simulations indicate that better performance is obtained in case of multi-objective genetic algorithm-optimized PID controller.
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References
Bhuvaneswari, N. S., G. Uma, and T. R. Rangaswamy. "Adaptive and optimal control of a non-linear process using intelligent controllers." Applied Soft Computing 9.1 (2009): 182–190. Elsevier Ltd.
Capón-García, Elisabet, Espuña, Antonio, Puigjaner, Luis: Statistical and simulation tools for designing an optimal blanketing system of a multiple-tank facility. Chemical Engineering Journal 152(1), 122–132 (2009)
B. Seth, D.S. J, “Liquid Level Control”, Control System Laboratory (ME413), IIT Bombay (2006–07).
Elke Laubwald, “Coupled Tanks Systems 1”, www.control-systems-principles.co.uk.
Åström, K. J., Albertos, P. and Quevedo, J. 2001. PID Control. Control Engineering Practice, 9,159–1161.J.
Norman S. Nise, 2003, Control System Engineering, 4\(^{th}\) Edition.
Goodwin, G.C., Graebe, S.F., Salgado, M.E.: Control System Design. Prentice Hall Inc., New Jersey (2001)
Larbes, C., Aït Cheikh, S. M., Obeidi, T., & Zerguerras, A. (2009). Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renewable Energy, Elsevier Ltd. \(34\)(10), 2093–2100.
MATLAB and SIMULINK Documentation.
Corriou, Jean-Pierre. Process Control: Theory and Applications. Springer. 2004. Page132-133.
Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. Systems, Man, and, Cybernetics SMC–16(1), 122–128 (1986)
Konak, Abdullah, Coit, David W., Smith, Alice E., et al.: Multi-objective optimization using genetic algorithm. Reliability Engineering and Safety System 91, 992–1007 (2006). Elsevier Ltd
Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Transactions of the ASME. 64, 759–768 (1942)
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Singh, S.K., Katal, N., Modani, S.G. (2014). Multi-Objective Optimization of PID Controller for Coupled-Tank Liquid-Level Control System Using Genetic Algorithm. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_7
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DOI: https://doi.org/10.1007/978-81-322-1602-5_7
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