Fuzzy Logic Techniques in Control Engineering
DOI:
https://doi.org/10.38017/2390058X.81Keywords:
fuzzy logic, fuzzy inference systems, fuzzy rules, fuzzy controller.Abstract
Fuzzy logic is understood as the way to represent reasoning and inaccurate or approximate ideas mathematically. It is based in input-output represented in a fuzzy rules set, which are linguistic expressions that associate causes with effects. Their versatility has made it suitable for solving reference following problems in control engineering, where have improved the performance of controllers for systems which are not necessarily linear and time invariant. In their theory several control schemes are studied in which fuzzy logic plays a determining role in its condition of adaptive control. Also, the capability fuzzy inference systems (FIS) have for dynamic systems identification contributes with solutions to control schemes that include reference models. Thus, fuzzy logic techniques in control engineering have been a successful alternative to aimed endeavors to improve performance of control systems towards nonlinearities, parameter variability and situations in which the process to control is inaccurate or not well known.
Author Biographies
Arnaldo Matute Clavier, Universidad Antonio Nariño – Sede Tunja
William Fernando Bernal Suárez, Fundación Universitaria Juan de Castellanos
References
L. A. Zadeh, “Pruf and its application to inference from fuzzy propositions”, in Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977 IEEE Conference on, Dec 1977, pp. 1359-1360. doi: https://doi.org/10.1109/CDC.1977.271515
[2] A. Matute, “Desarrollo de un generador de modelos difusos para la máquina de reluctancia conmutada”, Master’s thesis, Universidad Simón Bolívar, 2014.
[3] Mathworks. Matlab, “what is fuzzy logic?”, Help, 2014.
[4] E. M. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Neuro Fuzzy and Soft Computing. Prentice Hall, 1997. [En línea]. Disponible en: http://www.soukalfi.edu.sk/01_NeuroFuzzyApproach.pdf
[5] S. S. L. Chang and L. A. Zadeh, “On fuzzy mapping and control”, IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-2, no. 1, pp. 30-34, Jan 1972. doi: https://doi.org/10.1109/TSMC.1972.5408553
[6] J. Viola, “Control adaptativo de sistemas electrónicos de potencia con redes neuronales y criterio de estabilidad de lyapunov”, Ph.D. dissertation, Universidad Simón Bolívar, 2007.
[7] S. Tahmasebi, M. A. Khanesar, and M. Teshnehlab, “Adaptive direct fuzzy control of siso nonlinear systems using a fuzzy reference model”, in 2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), July 2016, pp. 93-98. doi: https://doi.org/10.1109/ACTEA.2016.7560119
[8] G. Shahgholian, M. Maghsoodi, M. Mahdavian, M. Janghorbani, M. Azadeh, and S. Farazpey, “Analysis of speed control in dc motor drive by using fuzzy control based on model reference adaptive control”, in 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (EC-TI-CON), June 2016, pp. 1-6. doi: https://doi.org/10.1109/ECTICon.2016.7561239
[9] B. Kosko, “Fuzzy systems as universal approximators,” IEEE Transactions on Computers, vol. 43, no. 11, pp. 1329-1333, Nov 1994. doi: https://doi.org/10.1109/12.324566
[10] A. Matute, J. Viola, J. Restrepo, J. M. Aller, and F. Quizhpi, “Switched reluctance machine fuzzy modeling applied on a mrac scheme”, in Circuits Systems (LASCAS), 2015 IEEE 6th Latin American Symposium on, Feb 2015, pp. 1-4. doi: https://doi.org/10.1109/LASCAS.2015.7250442
[11] A. Matute and M. Srefezza, “Sepic type dc-dc converter fuzzy model”, in ICCAS-SICE, 2009, Aug 2009, pp. 891-895. [En línea]. Disponible en: https://www.researchgate.net/profile/Arnaldo-Matute-Clavier/publication/241163945_SEPIC_type_DC-DC_converter_fuzzy_model/links/55d351bd08ae7fb244f58666/SEPIC-type-DC-DC-converter-fuzzy-model.pdf
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