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SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

PID Parameter Optimization By Using Ant Colony And Cuckoo Algorithms For Boost Converter
Önder Civelek1*, Metin Tüysüz2, Mustafa Şinasi Ayas3
1Karadeniz Technical University, Trabzon, Turkey
2Karadeniz Technical University, Trabzon, Turkey
3Karadeniz Technical University, Trabzon, Turkey
* Corresponding author: ondercivelek63@gmail.com
Published Date: 2019-12-22   |   Page (s): 246-252   |    181     5
https://doi.org/10.36287/setsci.4.6.069

ABSTRACT One of the most well-known and easy-to-design converter of DC-DC converters is DC-DC Boost converter. DC-DC boost converters are widely utilized in industrial applications. Therefore, output voltage regulation of the boost converter is a popular issue that researchers focus on. In this study, to improve output voltage of a Boost converter, Proportional-Integral-Derivative (PID) controllers of which the parameters are tuned by meta-heuristic optimization algorithms are designed. The Ant Colony Algorithm (ACO) and Cuckoo Search Algorithm (CSA) are the used optimization algorithms. The optimization algorithm results are compared to each other in addition to traditional trial-error method and simulation results are obtained in MATLAB/Simulink platform. The simulation results show that the optimized PID controller by both ACO and CSA is more effective in improving the transient and steady-state response of the DC-DC converter than the traditional tuning approach. In the optimization process, Integral Time-weighed Absolute Error (ITAE) performance metric is used as objective function. The performances of the controllers designed by ACO, CSA, and trial-error method are also compared to each other using Integral Squared Error (ISE), Integral Absolute Error (IAE) and ITAE performance metrics
KEYWORDS DC-DC Boost converter, Ant Colony Algorithm , Cuckoo Search Algorithm, PID, ITAE
REFERENCES [1] Dorigo, M., Optimization, Learning And Natural Algorithms, Ph.D. Thesis, Dipertimento Elettronica, Politecnico Di Milano, Milan, 1992.
[2] Köse, E., Mühürcü, A., Mühürcü, G., Ödemir M., PI Parameter Optimization By Fire Fly Algorithm For Optimal Controlling Of A Buck Converter's Output StateVariable. Sakarya University Journal of Science, 22 (5), 1267-1273, 2018
[3]Çoban, R., Erçin Ö. Multi-objective Bees Algorithm to Optimal Tuning of PID Controller. Cukurova Universitesi Muhendislik Mimarlık Fakultesi Dergisi, 27(2), ss. 13-26, Aralık 2012
[4]Almawlawe, M.D., Kovandzic, M. A Modified Method for Tuning PID Controller for Buck-Boost Converter. International Journal of Advanced Engineering Research and Science (IJAERS). Vol-3, Issue-12, Dec- 2016
[5]Pourhossein, H.,Zare, A.,Monfared, M. Hybrid Modeling and PID-PSO Control of Buck-Boost Chopper. https://www.researchgate.net/publication/
295827196. January 2012
[6] Uygur, S., Simulation and Implementation of a Soft Switched DC-DC Boost Converter for Photovoltaic Systems, Master Thesis, Yıldız Technical University, Institute of Science and Technology, Istanbul, 2011.
[7] Karaboğa, D., Artificial Intelligence Optimization Algorithms, Extended Second Edition, Nobel Yayın Dağıtım, Ankara, 2011.
[8] Yang, X., S., Nature-Inspired Metaheuristic Algorithms, Second Edition, Luniver Press, Frome, 2010.
[9] Gangal, V., Antithesis of Energy Efficient Routing by Ant Colony Algorithm Routing in Wireless Sensor Networks, Master Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon, 2015.
[10] Dikmen, H., Dikmen, H., Elbir, A., Ekşi, Z. Ve Çelik, F. 2014., Optimization and Comparison of Traveling Salesman Problem with Ant Colony and Genetic Algorithms. Süleyman Demirel University Journal of the Institute of Science and Technology, 18:1, 8-13.
[11] Westcott, J.H., 1954. The Minimum Moment Of Error Squared Criterion: A New Performance Criterion For Servo Mechanisms. Measurements Section, 101(83), 471-480.
[12] Mahony, T.O., Downing, C.J., Fatla, K., 2000. Genetic Algorithm For Pid Parameter Optimization: Minimizing Error Criteria. Process Control And Instrumentation, 26-28 Temmuz, İskoçya, 148-153.
[13] Mitsukura, Y., Yamamoto, T., Kaneda, M., 1999. A Design Of Self-Tuning Pid Controllers Using A Genetic Algorithm. American Control Conference, 2-4 Haziran, San Diego, 1361 – 1365.
[14] Lieslehto, J., 2001. Pid Controller Tuning Using Evolutionary Programming. American Control Conference, 25-27 Haziran, Abd, 2828-2833.


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