Genetic Algorithm for the Optimization of an Electrolyte Flow Controller in a Non-conventional Manufacturing Method Optimización genética del control de flujo de electrolito en un método de manufactura no convencional
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mar 19, 2025
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https://doi.org/10.30878/ces.v32n0a38
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Irvin Uriel Nopalera Angeles
http://orcid.org/0000-0002-1186-6606
Everardo Efrén Granda Gutiérrez
http://orcid.org/0000-0002-9316-9627
René Arnulfo García Hernández
http://orcid.org/0000-0001-7941-377X
Ángel Hernández Castañeda
http://orcid.org/0000-0002-2811-0813
Roberto Alejo Eleuterio
http://orcid.org/0000-0002-7580-3305
http://orcid.org/0000-0002-1186-6606
Everardo Efrén Granda Gutiérrez
http://orcid.org/0000-0002-9316-9627
René Arnulfo García Hernández
http://orcid.org/0000-0001-7941-377X
Ángel Hernández Castañeda
http://orcid.org/0000-0002-2811-0813
Roberto Alejo Eleuterio
http://orcid.org/0000-0002-7580-3305
Resumen
This work describes the optimization of a fuzzy controller using a genetic algorithm based on Darwin's principle of natural selection and Mendel's inheritance laws. An elitist selection, blend alpha crossover, and uniform mutation are implemented, with crossover rates of 60 and 80 %, evaluating its performance statistically. The chromosome with the best fitness is implemented in an electrolyte flow controller for an electrochemical machining process. The result is compared with a classical Proportional-Integral-Derivative controller tuned with Ziegler-Nichols, showing that the optimized regulator achieves better performance, with a 7.95 % overshoot, a steady-state error of 0.1151, and a settling time of 4.8 s.
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NOPALERA ANGELES, Irvin Uriel et al.
Genetic Algorithm for the Optimization of an Electrolyte Flow Controller in a Non-conventional Manufacturing Method.
CIENCIA ergo-sum, [S.l.], v. 32, mar. 2025.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/23332>. Fecha de acceso: 27 abr. 2025
doi: https://doi.org/10.30878/ces.v32n0a38.
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Ciencias exactas y aplicadas
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
Abbas, M., Tiun, S., Ayob, M., & Dhief, F. (2020). Genetic algorithm based on natural selection theory for optimization problems. Symmetry, 12(11), 1-31. https://doi.org/10.3390/sym12111758
Dogruer, T., & Can, M. (2022). Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm. Transactions of the Institute of Measurement and Control, 44(9), 1862-1873. https://doi.org/10.1177/01423312211066758
Dumitrescu, C., Ciotirnae, P., & Vizitiu, C. (2021). Fuzzy logic for intelligent control system using soft computing applications. Sensors, 21(8), 2617. https://doi.org/10.3390/s21082617
Ellis, G. (2012). Four types of controllers. In Control system design guide (4th ed., pp. 97–119). Butterworth-Heinemann. https://doi.org/10.1016/b978-0-12-385920-4.00006-0
Errouha, M., Derouich, A., Motahhir, S., Zamzoum, O., Ouanjli, N., & Ghzizal, A. (2019). Optimization and control of water pumping PV systems using fuzzy logic controller. Energy Reports, 5, 853-865. https://doi.org/10.1016/j.egyr.2019.07.001
Frota, R., Tanscheit, R., & Vellasco, M. (2022). Fuzzy logic for control of injector wells flow rates under produced water reinjection. Journal of Petroleum Science and Engineering, 215, 1-26. https://doi.org/10.1016/j.petrol.2022.110574
Ghaleb, A., Oglah, A., Humaidi, A., Al-Obaidi, A., & Ibraheem, I. (2023). Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum. International Review of Applied Sciences and Engineering, 14(1), 1-12. https://doi.org/10.1556/1848.2021.00375
Golpîra, H., Román, A., & Bevrani, H. (2021). Renewable integrated power system stability and control. Wiley. https://doi.org/10.1002/9781119689836
Grzesik, W. (2017). Introduction. In Advanced machining processes of metallic materials. Elsevier. https://doi.org/10.1016/b978-0-444-63711-6.00001-6
Kant, S., Agarwal, D., & Shukla, P. (2021). A survey on fuzzy systems optimization using evolutionary algorithms and swarm intelligence. In Computer Vision and Robotics: Proceedings of CVR 2021 (pp. 421-444). Springer. https://doi.org/10.1007/978-981-16-8225-4_33
Liu, G., Tong, H., Li, Y., Zhong, H., & Tan, Q. (2020). Multiphysics research on electrochemical machining of micro holes with internal features. The International Journal of Advanced Manufacturing Technology, 110(5), 1527-1542. https://doi.org/10.1007/s00170-020-05973-9
Natsu, W. (2018). Micro Electrochemical Machining. In Yan, J. (Ed.) Micro and Nano Fabrication Technology. (pp. 807-855). 1, Springer. https://doi.org/10.1007/978-981-10-6588-0_26-1
Nopalera, I. (2021). Algoritmo de control difuso para el ajuste de polarización de un proceso de maquinado electroquímico por pulsos. [Tesis de maestría. Universidad Autónoma del Estado de México]. Biblos-e File. http://hdl.handle.net/20.500.11799/112256
Ortega, R., Romero, J., Borja, P., & Donaire, A. (2021). PID Passivity-Based Control of Nonlinear Systems with Applications. Wiley. https://doi.org/10.1002/9781119694199
Piraisoodi, T., Iruthayarajan, W., & Abdul, M. (2018). Multi-objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques. Expert Systems, 36(2), 1-15. https://doi.org/10.1111/exsy.12366
Qin, Y. (2015). Micromanufacturing Engineering and Technology (2nd ed). William Andrew. ISSN: 9780323311496
Reddy, A., Agarwal, P., & Chand, S. (2018). Adaptive multipopulational genetic algorithm based self-designed fuzzy logic controller for active magnetic bearing application. International journal of dynamics and control, 6, 1392-1408. https://doi.org/10.1007/s40435-017-0357-z
Sato, M., & Oyama, A. (2021). Comparative Study of Crossovers for Decision Space Diversity of Non-Dominated Solutions. In IEEE Symposium Series on Computational Intelligence (pp. 1-8). IEEE. https://doi.org/10.1109/SSCI50451.2021.9660042
Takahashi, M., & Kita, H. (2001). A crossover operator using independent component analysis for real-coded genetic algorithms. In Proceedings of the 2001 Congress on Evolutionary Computation (pp. 643-649). IEEE. http://doi.org/10.1109/CEC.2001.934452
Xu, Z., & Wang, Y. (2021). Electrochemical machining of complex components of aero-engines developments, trends and technological advances. Chinese Journal of Aeronautics, 34(2), 28-53. https://doi.org/10.1016/j.cja.2019.09.016
Yazid, E., Garratt, M., & Santoso, F. (2019). Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots. Applied Soft Computing, 78, 373-392. https://doi.org/10.1016/j.asoc.2019.02.023
Ye, Y., Wang, Z., & Zhang, X. (2020). An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error. Structural and Multidisciplinary Optimization, 62, 529-542. https://doi.org/10.1007/s00158-020-02508-4
Zangeneh, M., Aghajari, E., & Forouzanfar, M. (2022). A review on optimization of fuzzy controller parameters in robotic applications. IETE Journal of Research, 68(6), 4150-4159. https://doi.org/10.1080/03772063.2020.1787878
Dogruer, T., & Can, M. (2022). Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm. Transactions of the Institute of Measurement and Control, 44(9), 1862-1873. https://doi.org/10.1177/01423312211066758
Dumitrescu, C., Ciotirnae, P., & Vizitiu, C. (2021). Fuzzy logic for intelligent control system using soft computing applications. Sensors, 21(8), 2617. https://doi.org/10.3390/s21082617
Ellis, G. (2012). Four types of controllers. In Control system design guide (4th ed., pp. 97–119). Butterworth-Heinemann. https://doi.org/10.1016/b978-0-12-385920-4.00006-0
Errouha, M., Derouich, A., Motahhir, S., Zamzoum, O., Ouanjli, N., & Ghzizal, A. (2019). Optimization and control of water pumping PV systems using fuzzy logic controller. Energy Reports, 5, 853-865. https://doi.org/10.1016/j.egyr.2019.07.001
Frota, R., Tanscheit, R., & Vellasco, M. (2022). Fuzzy logic for control of injector wells flow rates under produced water reinjection. Journal of Petroleum Science and Engineering, 215, 1-26. https://doi.org/10.1016/j.petrol.2022.110574
Ghaleb, A., Oglah, A., Humaidi, A., Al-Obaidi, A., & Ibraheem, I. (2023). Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum. International Review of Applied Sciences and Engineering, 14(1), 1-12. https://doi.org/10.1556/1848.2021.00375
Golpîra, H., Román, A., & Bevrani, H. (2021). Renewable integrated power system stability and control. Wiley. https://doi.org/10.1002/9781119689836
Grzesik, W. (2017). Introduction. In Advanced machining processes of metallic materials. Elsevier. https://doi.org/10.1016/b978-0-444-63711-6.00001-6
Kant, S., Agarwal, D., & Shukla, P. (2021). A survey on fuzzy systems optimization using evolutionary algorithms and swarm intelligence. In Computer Vision and Robotics: Proceedings of CVR 2021 (pp. 421-444). Springer. https://doi.org/10.1007/978-981-16-8225-4_33
Liu, G., Tong, H., Li, Y., Zhong, H., & Tan, Q. (2020). Multiphysics research on electrochemical machining of micro holes with internal features. The International Journal of Advanced Manufacturing Technology, 110(5), 1527-1542. https://doi.org/10.1007/s00170-020-05973-9
Natsu, W. (2018). Micro Electrochemical Machining. In Yan, J. (Ed.) Micro and Nano Fabrication Technology. (pp. 807-855). 1, Springer. https://doi.org/10.1007/978-981-10-6588-0_26-1
Nopalera, I. (2021). Algoritmo de control difuso para el ajuste de polarización de un proceso de maquinado electroquímico por pulsos. [Tesis de maestría. Universidad Autónoma del Estado de México]. Biblos-e File. http://hdl.handle.net/20.500.11799/112256
Ortega, R., Romero, J., Borja, P., & Donaire, A. (2021). PID Passivity-Based Control of Nonlinear Systems with Applications. Wiley. https://doi.org/10.1002/9781119694199
Piraisoodi, T., Iruthayarajan, W., & Abdul, M. (2018). Multi-objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques. Expert Systems, 36(2), 1-15. https://doi.org/10.1111/exsy.12366
Qin, Y. (2015). Micromanufacturing Engineering and Technology (2nd ed). William Andrew. ISSN: 9780323311496
Reddy, A., Agarwal, P., & Chand, S. (2018). Adaptive multipopulational genetic algorithm based self-designed fuzzy logic controller for active magnetic bearing application. International journal of dynamics and control, 6, 1392-1408. https://doi.org/10.1007/s40435-017-0357-z
Sato, M., & Oyama, A. (2021). Comparative Study of Crossovers for Decision Space Diversity of Non-Dominated Solutions. In IEEE Symposium Series on Computational Intelligence (pp. 1-8). IEEE. https://doi.org/10.1109/SSCI50451.2021.9660042
Takahashi, M., & Kita, H. (2001). A crossover operator using independent component analysis for real-coded genetic algorithms. In Proceedings of the 2001 Congress on Evolutionary Computation (pp. 643-649). IEEE. http://doi.org/10.1109/CEC.2001.934452
Xu, Z., & Wang, Y. (2021). Electrochemical machining of complex components of aero-engines developments, trends and technological advances. Chinese Journal of Aeronautics, 34(2), 28-53. https://doi.org/10.1016/j.cja.2019.09.016
Yazid, E., Garratt, M., & Santoso, F. (2019). Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots. Applied Soft Computing, 78, 373-392. https://doi.org/10.1016/j.asoc.2019.02.023
Ye, Y., Wang, Z., & Zhang, X. (2020). An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error. Structural and Multidisciplinary Optimization, 62, 529-542. https://doi.org/10.1007/s00158-020-02508-4
Zangeneh, M., Aghajari, E., & Forouzanfar, M. (2022). A review on optimization of fuzzy controller parameters in robotic applications. IETE Journal of Research, 68(6), 4150-4159. https://doi.org/10.1080/03772063.2020.1787878