THE USE OF FUZZY SETS OF THE SECOND TYPE FOR THE DESIGN OF SPEED CONTROLLERS
DOI:
https://doi.org/10.26906/SUNZ.2024.3.029Keywords:
interval fuzzy set of the second type, fuzzy systems, speed control, decision support system, unclear regulatorsAbstract
Interval fuzzy sets of the second type are used in the design of fuzzy systems related to speed control under conditions of uncertainty. The article considers the possibilities of using interval fuzzy sets of the second type to describe variables that can be different values of the same dynamic system or changes in technical parameters that occur under the influence of external factors. Complex interval fuzzy sets of the second type are also presented and examples of their use for reducing the number of fuzzy sets and rules in the fuzzy knowledge base are given. The necessary conditions for constructing complex interval fuzzy sets of the second type and methods for determining the lower and upper membership functions in them are also given. The uncertainty trace for a complex interval fuzzy set of the second type is represented by the upper and lower membership functions of different fuzzy sets. All the methods described in the work can be used in the design of decision support systems, to select the optimal parameters of a nonlinear system, or in the design of fuzzy speed controllers.Downloads
References
Reddy, P. V. S. Fuzzy logic based on Belief and Disbelief membership functions. Fuzzy Information and Engineering, 2017, 9(4), pp. 405-422, doi: 10.1016/j.fiae.2017.12.001.
Babanezhad, M., Masoumian, A., Nakhjiri, A. T. Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system. Scientific Reports, 2020, vol. 10(1), pp. 1-20, doi: 10.1038/s41598-020-73175-0.
Pelalak, R., Nakhjiri, A. T., Marjani, A. Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors. Scientific Reports, 2021, 11(1), pp.1-11, doi:10.1038/s41598-021-81514-y.
Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A., Soria, D., Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 2020, 29(5), pp.1160- 1172, doi: 10.1109/TFUZZ.2020.2969901.
Zadeh, L. A. The concept of a linguistic variable and its application to approximate reasoning-I. Information sciences, 1975, vol. 8(3), pp.199-249. doi: 10.1016/0020-0255(75)90036-5.
Liang Q., Mendel J. M. Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems, 2000, 8(5), pp. 535-550, doi: 10.1109/91.873577.
Zakovorotniy A.Y. Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm / A.Y. Zakovorotniy, V.D. Dmitrienko, S.Yu. Leonov, I.P. Khavina // The Open Neurology Journal. – 2014. – № 8. – P. 15 – 21.
Mendel, J. M. Type-2 fuzzy sets: some questions and answers. IEEE Connections, Newsletter of the IEEE Neural Networks Society, 2003, 1, pp. 10-13.
Deveci, M., Cali, U., Kucuksari, S. and Erdogan, N. Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland. Energy, 2020, p.117317, doi:10.1016/j.energy.2020.117317.
Biswas, R., Sil, J. An improved canny edge detection algorithm based on type-2 fuzzy sets. Procedia Technology, 2012, 4, pp. 820-824, doi: 10.1016/j.protcy.2012.05.134.
Zhao, F., Chen, Y., Liu, H., Fan, J. Alternate PSO-based adaptive interval type-2 intuitionistic fuzzy C-means clustering algorithm for color image segmentation. IEEE Access, 2019, 7, pp. 64028-64039, doi: 10.1109/ACCESS.2019.2916894.
Yatak, M. Ö., Şahin, F. Ride Comfort-Road Holding Trade-off Improvement of Full Vehicle Active Suspension System by Interval Type-2 Fuzzy Control. Eng. Science and Techn., 2021, 24(1), pp. 259-270, doi: doi.org/10.1016/j.jestch.2020.10.006.
Zakovorotniy, A., Kharchenko, A. Optimal Speed Controller Design with Interval Type-2 Fuzzy Sets. In 2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek), 2021 pp. 363-366, doi:10.1109/KhPIWeek53812.2021.9570045.
Заковоротний О.Ю. Синтез оптимальних законів управління рухом дизель-поїзда за допомогою математичної моделі у формі Бруновського / О.Ю. Заковоротний, В.Д. Дмитрієнко, Н.В. Мезенцев // Інформаційно-керуючі системи на залізничному транспорті. – Харків, 2010. – № 5-6 (84-85). – C. 7 – 13.
