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Control basado en lógica difusa: De los fundamentos a las aplicaciones

Autores/as

DOI:

https://doi.org/10.37135/ns.01.08.01

Palabras clave:

Conjuntos Difusos, Sistemas de Inferencia Difusos, Defusificación, Control Difuso, Control de Procesos

Resumen

Desde sus inicios la lógica Difusa ha demostrado su utiidadl en distintas áreas del conocimiento; para el caso particular de sistemas de control de procesos, desde la construcción de modelos de procesos, hasta el diseño de controladores avanzados ha probado su efectividad e importancia en aplicaciones industriales. En este artículo se presenta una guía para el estudio de sistemas basados en lógica difusa enfocados al control de procesos, iniciando con  sus origenes,  fundamentos teóricos y una guía para la construcción de algunos controladores de lazo implementados sobre una planta benchmark

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Referencias

Abilov, A. G., Zeybek, Z., Tuzunalp, O., & Telatar, Z. (2002). Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems. Chemical Engineering and Processing: Process Intensification, 41(1), 87–98. https://doi.org/10.1016/S0255-2701(01)00119-2

Abonyi, J. (2003). Fuzzy Model Identification for Control. Birkhauser, Boston: Bruce Postlethwaite. https://doi.org/10.1002/rnc.882

Aboukheir, H., Herrera, M., Chavez, D., Leica, P., & Camacho, O. (2020). Fuzzy adaptive MPC for nonlinear time varying delayed systems. 2020 IEEE ANDESCON. Quito, Ecuador: IEEE publisher. https://doi.org/10.1109/ANDESCON50619.2020.9272058

Aboukheir, H (2010). Closed loop identification using Takagi Sugeno models. IEEE Latin America Transactions, 8(3), 199-204. https://doi.org/10.1109/TLA.2010.5538393

Al-Hadithi, B. M., Jiménez, A., & Matía, F. (2012). A new approach to fuzzy estimation of Takagi–Sugeno model and its applications to optimal control for nonlinear systems. Applied Soft Computing, 12(1), 280-290. http://dx.doi.org/10.1016%2Fj.asoc.2011.08.044

Babaei, A. R., & Setayandeh, M. R. (2018). A new approach for robust design optimization based on the concepts of fuzzy logic and preference function. Journal of Aerospace Technology and Management, 10. Retrieve from https://www.researchgate.net/publication/325397650_A_New_Approach_for_Robust_Design_Optimization_Based_on_the_Concepts_of_Fuzzy_Logic_and_Preference_Function

Barreiro, A., & Baños, A. (2012). Sistemas de control basados en reset. Revista Iberoamericana de Automática e Informática Industrial, 9(4), 329-346. https://www.elsevier.es/es-revista-revista-iberoamericana-automatica-e-informatica-331-articulo-sistemas-control-basados-reset-S1697791212000659

Bedoud, K. Ali-rachedi, M. Bahi, T., & Lakel, R. (2015). Adaptive fuzzy gain scheduling of PI controller for control of the wind energy conversion systems. Energy Procedia 71. 211-225. Retrieved from https://core.ac.uk/download/pdf/82540857.pdf

Camacho, O., & Smith, C. (2000). Sliding mode control: an approach to regulate nonlinear chemical processes. ISA Transactions, 39(2), 205-218. https://doi.org/10.1016/S0019-0578(99)00043-9

Chen, J-Y. (1997). Design of a SMC-based fuzzy controller for nonlinear systems. IEEE International Conference on Fuzzy Systems, 1, 377–382. https://doi.org/10.1109/FUZZY.1997.616398

Cox, E. (2005), Fuzzy Modeling and Genetic Algorithms for Mining and Exploration. Elsiever, SF:CA. Retrieved from https://books.google.com.ec/books?id=-ARBk8j5Al8C&printsec=frontcover#v=onepage&q&f=false

Doctor, F., Syue, C.-H., Liu, Y.-X., & Shieh, J.-S. (2015). Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia, Applied Soft Computing Journal, 38, 872-889. http://dx.doi.org/10.1016/j.asoc.2015.10.014

Garcia, C. E., & Morari, M. (1982). Internal model control. Part 1: A unifying review and some new results. Industrial Engineering and Chemical Process Design and Development, 21(2), 681–699. Retrieved from https://www.academia.edu/4557007/Internal_model_control_A_unifying_review_and_some_new_results

Greg, F. (2000). PID Control. CRC Press, Retrieved from http://dsp-book.narod.ru/MISH/CH97.PDF

Ha, Q. P., Nguyen, H. Q., Rye, D. C., & Durrant-Whyte, H. F. (1998). Sliding mode control with fuzzy tuning for an electro-hydraulic position servo system. Proceeding of International Conference on Knowledge-Based Intelligent Electronic Systems (pp. 141-148). Adelaide, SA, Australia. https://doi.org/10.1109/KES.1998.725838

Harliana, P., & Rahim, R. (2017). Comparative analysis of membership function on Mamdani fuzzy inference system for decision making. Journal of Physics: Conference Series, 930(1), 012029. Retrieved from https://iopscience.iop.org/article/10.1088/1742-6596/930/1/012029/pdf

Henson, M. A., & Seborg, D. E. (1994). Adaptive nonlinear control of a pH neutralization process. IEEE Transactions on Control Systems Technology, 2(3), 169–182. https://doi.org/10.1109/87.317975

Herrera, M., Sarszosa, M., Paredes, I.,& Camacho, O., (2019). Optimal control based on fuzzy estimation of takagi-sugeno model for the furuta pendulum: Experimental results. WSEAS Transactions on Systems 18, 12–24. Retrieved form https://www.researchgate.net/profile/Marco-Herrera-4/publication/332057259_Optimal_Control_Based_on_Fuzzy_Estimation_of_Takagi-Sugeno_Model_for_the_Furuta_Pendulum_Experimental_Results/links/5cc6e916a6fdcc1d49b7e6c4/Optimal-Control-Based-on-Fuzzy-Estimation-of-Takagi-Sugeno-Model-for-the-Furuta-Pendulum-Experimental-Results.pdf

Hoang, N. D. (2020). Image processing-based pitting corrosion detection using metaheuristic optimized multilevel image thresholding and machine-learning Approaches Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6765274

Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft compuiting: A Computational Approach to Learning and Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall.

Jantzen, J. (1998). Tuning of Fuzzy PID controllers. Retrieved from https://www.cic.ipn.mx/~pescamilla/ContInt/Jantzen1998.pdf

Jiang, B., Gao, Z., Shi, P., & Xu, Y. (2010). Adaptive fault-tolerant tracking control of near-space vehicle using Takagi–Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems, 18(5), 1000-1007. https://dx.doi.org/10.1109/tfuzz.2010.2058808

Jiménez, A., Al-Hadithi, B. M., & Matía, F. (2008). An optimal TS model for the estimation and identification of nonlinear functions. WSEAS Transactions on Systems and Control, 3(10), 897-906. Retrieved from http://www.wseas.us/e-library/transactions/control/2008/28-596.pdf

Kavsek-Biasizzo, K., Skrjanc, I., & Matko, D. (1997). Fuzzy predictive control of highly nonlinear pH process. Computers Chem. Eng, 21, S613–S618. Retrieved from http://msc.fe.uni-lj.si/Papers/CCE_Kavsek1997.pdf

Klug, M., Castelan, E. B., Leite, V. J. S., & Silva, L. F. P. (2015). Fuzzy dynamic output feedback control through nonlinear Takagi–Sugeno models. Fuzzy Sets and Systems, 263(C), 92-111. https://doi.org/10.1016/j.fss.2014.05.019

Liu, F., Heiner, M., & Yang, M. (2016). Fuzzy stochastic Petri Nets for modeling biological systems with uncertain kinetic parameters. PLOS ONE, 11(2). https://doi.org/10.1371/JOURNAL.PONE.0149674

Lisauskas, S., & Rinkeviciene, R. (2011). Fuzzy adaptive PID control design. Retrieved from http://azadproject.ir/wp-content/uploads/2013/07/Fuzzy-adaptive-PID-control.pdf

Ljung, L (1999). System Identification: Theory for the user (2nd ed.). Prentice Hall PTR, Upper Saddle River. https://doi.org/10.1002/047134608X.W1046

Lu, Y-S. , Chen, J. (1994). A self-organizing fuzzy sliding mode controller design for a class of nonlinear servo systems. IEEE Transactions on Industrial Electronics, 41, 492–502. https:/doi.org/10.1109/41.315267

Meda-Campana, J. A., Gómez-Mancilla, J. C., & Castillo-Toledo, B. (2011). Exact output regulation for nonlinear systems described by Takagi–Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems, 20(2), 235-247. https://doi.org/10.1109/TFUZZ.2011.2172689

Mehra, R. K., Rouhani, R., Eterno, J., Richalet, J. & Rault, A. (1982). Model algorithmic control: Review and recent developments. Engineering Foundation Conference on Chemical Process Control II, 287–310.

Meier, R., Nieuwland, J., Hacisalihzade, S. S., & Zbinden, A. M. (1992). Fuzzy logic control of blood pressure during anesthesia. IEEE Control Systems, 12(6), 12–17. https://doi.org/10.1109/37.168811

Melin, P., & Castillo, O. (2007). An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory. Information Sciences, 177(7), 1543–1557. https://doi.org/10.1016/J.INS.2006.07.022

Menzl, S., Stuhler, M., & Benz, R. (1996). A self adaptive computer-based pH measurement and fuzzy-control system. Water Research, 30(4), 981–991. https://doi.org/10.1016/0043-1354(95)00249-9

Mizumoto, M (1995). Realization of PID controls by fuzzy control methods. Fuzzy Sets and Systems. 70(2-3). 171-182. https://doi.org/10.1016/0165-0114(94)00215-S

Modi, K. P., Sahin, F., & Saber, E. (2005). An application of human robot interaction: Development of a ping-pong playing robotic arm. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2, 1831–1836. https://doi.org/10.1109/ICSMC.2005.1571413

Qin, Y., Sun, L., Hua, Q., & Liu, P. (2018). A fuzzy Adaptive PID controller design for fuel cell power plant. Sustainaibility, 10(7). https://doi.org/10.3390/su10072438

Reznick, L. (1997). Fuzzy Controllers. Oxford, Boston: Newness Press. Retrieved from https://research.iaun.ac.ir/pd/naghsh/pdfs/UploadFile_4810.pdf

Siddique, N (2014). Intelligent Control: A Hybrid approach based on Fuzzy Logic, Neural Networks and Genetic Algorithm. NY: Sprimger Cham Heidelberg. https://doi.org/10.1007/978-3-319-02135-5

Takagi, T. & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), pp. 116-132. https://doi.org/10.1109/TSMC.1985.6313399

Tzafestas S. G. & Rigatos, G. G. (1999). A simple robust sliding mode fuzzy logic controller of the diagonal type. Journal of Intelligence and Robotic Systems, 26, 353–388. https://doi.org/10.1023/A:1008161815798

Vaija, P., Turunen, I., Jarvelainen, M., & Dohnal, M. (1985). Fuzzy strategy for failure detection and safety control of complex processes. Microelectronics Reliability, 25(2), 369–381. https://doi.org/10.1016/0026-2714(85)90024-1

Waewsak, C., Nopharatana, A., & Chaiprasert, P. (2010). Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production. Journal of Environmental Sciences, 22(12), 1883–1890. https://doi.org/10.1016/S1001-0742(09)60334-X

Wang, L (2009). Model predictive control systems design and implementation using Matlab®. London, UK: Springer-Verlag. https://doi.org/10.1007/978-1-84882-331-0

Yesil, E. Guzelkaya, M. & Eksin, I. (2003) Fuzzy PID controllers: An overview. The Third Triennial ETAI conference on applied automatic systems. Macedonia. Retrieved from https://www.researchgate.net/publication/255567860_Fuzzy_PID_controllers_An_overview

Zadeh, L. (1965). Fuzzy sets. Inform. Control, 8, 338–353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X

Zhao, Z.-Y., Tomizuka, M., & Isaka, S. (1993). Fuzzy gain scheduling of PID controllers. IEEE Transactions on Systems Man and Cybernetics, 23(5), 1392-1398. https://doi.org/10.1109/21.260670

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2021-12-01

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Control basado en lógica difusa: De los fundamentos a las aplicaciones. (2021). Novasinergia, ISSN 2631-2654, 4(2), 06-37. https://doi.org/10.37135/ns.01.08.01