Е-Публикации
Технически университет - София

Abstract: The paper focuses on the development of an approach for performance improvement of control systems by designing interval type-2 (IT2) PID fuzzy logic controllers (FLC) with single and two inputs. The approach is demonstrated for the control of the temperature in a laboratory fruit dryer and conforms to the requirements for real-time control via industrial programmable logic controllers. It steps on the integration of IT2 FLC empirical design with optimisation using genetic algorithms. The suggested performance-bound fitness function is computed from simulations of the FLC closed-loop systems using a Takagi-Sugeno-Kang plant model derived from experimental step responses. The optimised FLC parameters are the pre-and post-processing gains and selected output singletons and parameters of the upper membership functions that shape the footprint of the uncertainty of the IT2 PID FLC. The designed IT2 PID FLC control systems outperform in simulations the corresponding type-1 PID FLC systems in an increased dynamic accuracy and smoothness of the control action.

References

1. Ataşlar-Ayyıldız, B., Karahan, O. (2020). Design of a MAGLEV system with PID-based fuzzy control using CS algorithm. Cybern. and Technol. 20(5), 5-19
2. Chao, C.-T., Sutarna, N., Chiou, J.-S., Wang, C.-J. (2019). An optimal fuzzy PID controller design based on conventional PID control and nonlinear factors. Appl. Sci. 9(6) 1224, 1-18.
3. Nursoy, H., Başçic, A. (2024). Comparison of the performance of type-1 and interval type-2 fuzzy PI controllers for liquid level control in a coupled tank system. Journal of Sci and Technol. 17(2), 523-542.
4. Saad, M., Alshara, M., Mustafa, K. (2023). Fuzzy PID controller design for a coupled tank liquid level control system. WSEAS Transactions on Systems and Control, 18, 401-408. https://doi.org/10.37394/23203.2023.18.42.
5. Belman-Flores, J.M.;Rodríguez-Valderrama, D.A., Ledesma, S.; García-Pabón, J.J., Hernández, D., Pardo-Cely, D.M. (2022). A review on applications of fuzzy logic control for refrigeration systems. Appl. Sci. 12, 1302.
6. Berouine, A., Akssas, E., Naitmalek, Y., Lachhab, F., Bakhouya, M., Ouladsine, R., Essaaidi, M. (2019). A fuzzy logic-based approach for HVAC systems control. 2019 6th Int. Conf. on Contr., Decision and Inform. Technol. (CoDIT), Paris, France, 1510-1515.
7. Chojecki, A.; Ambroziak, A.; Borkowski, P. (2023). Fuzzy controllers instead of classical PIDs in HVAC equipment: dusting off a well-known technology and today’s implementation for better energy efficiency and user comfort. Energies 16, 2967.
8. Sellitto, M.A., Balugani, E., Gamberini, R., Rimini, B. (2018). A Fuzzy logic control application to the cement industry. IFAC-Papers OnLine 51, 1542-1547.
9. Vasičkaninová, A., Bakošová, M., Mészáros, A. (2021). Fuzzy control design for energy efficient heat exchanger network, Chem. Eng. Trans. 88, 529-534.
10. Vivekanandan, N., Fulambarkar, Dr. A.M., Waghmare, S. (2020). Experimental validation of fuzzy logic based anti-lock braking system used in quarter car model. Int. J. of Contr. and Autom. 13(2), 332–348.
11. Yanti, N., Nur, T., and Randis, R. (2022). Implementation of fuzzy logic in fish dryer design. ILKOM Journal Ilmiah 14(1), 39-51.
12. 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. Appl. Soft Comp., 78, 373-392.
13. Ye, L., Liang, C., Li, X. and Li, D. (2020). Energy efficiency improvement of eddy-current braking and heating system for electric bus based on fuzzy control. IET Electr. Syst. Transp. 10(10), 385-390.
14. Yordanova, S. Design of Fuzzy Supervisor-based Adaptive Process Control Systems. In: New Approaches in Intelligent Control: Techniques, Methodologies and Applications, Book series “Intelligent Systems Reference Library”, Eds: K. Nakamatsu, R. Kountchev, Springer Int. Publishing, Switzerland, Chapter 1, 1-42, 2016, ISBN: 978-3-319-32166-0.
15. Ahmad, S., Ali, S., Tabasha, R. (2020). The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control. Eng. Sci. and Technol.-an International Journal 23, 307–315.
16. Aydogmus, Z. (2015). A real-time robust fuzzy-based level control using programmable logic controller. Elektronika ir Elektrotechnika 21 (1), 13-17.
17. Yordanova S., Stoitseva-Delicheva D. (2024). Prospects of intelligent techniques for energy efficient control of drying process, Proc. IEEE Int. conf. Automatics and Informatics’2024 (ICAI’24), Oct.10-12, Varna, Bulgaria, pp. 205-211.
18. Yordanova, S., Stoitseva-Delicheva, D. (2025). TSK model-based energy efficient control with application to temperature in laboratory fruit dryer. Journal of Electrical Systems (JES), Vol. 20 No. 3 (2024), https://doi.org/10.52783/jes.8397.
19. Feng, G. Analysis and Synthesis of Fuzzy Control Systems: A Model Based Approach. Taylor & Francis, Bosa Roca, US., 2017.
20. Civelek, Z. (2020). Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Eng. Sci. and Technol.-an International Journal 23(1), 1-9, ISSN: 2215-0986.
21. Hosseinpour, S., Martynenko, A. (2022). An adaptive fuzzy logic controller for intelligent drying, Dry. Technol. 41(7).
22. Venkataraman, A. (2021). Design and implementation of adaptive PID and adaptive fuzzy controllers for a level process station. Adv. in Technol. Innov. 6(2), 90-105.
23. Raj, R., Mohan, B.M. (2020). General structure of interval type-2 fuzzy PI/PD controller of Takagi–Sugeno type. Eng. Appl. of Artificial Intell. 87, 103273.
24. Wu, D., Mendel, J. M. (2019). Recommendations on designing practical interval type-2 fuzzy systems. Eng. Appl. of Artificial Intell. 85, 182–193.
25. Nie, M., Tan, W.W. (2008). Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. Proc. IEEE Int. Conf. on Fuzzy Syst., Hong Kong, China, 1–6 June 2008, 1425–1432.
26. Al-Mahturi, A., Santoso, F., Garratt, M. A., Anavatti S. G. (2019). An intelligent control of an inverted pendulum based on an adaptive interval type-2 fuzzy inference system, Proc. 2019 IEEE Int. Conf. on Fuzzy Syst. (FUZZ-IEEE), New Orleans, LA, USA, 1-6.
27. El-Nagar, A. M., El-Bardini, M. (2014). Practical implementation for the interval type-2 fuzzy PID controller using a low cost microcontroller, Ain Shams Eng. Journal 5, 475–487.
28. El-Sotouhy, M. M., Mansour, A. A.,. Marei, M. I., Zaki, A. M., El-Sattar, A. A. (2020). A comparative study between type-1 and type-2 fuzzy logic controllers for 4-leg active power filter. Int. J. of Contr. and Autom., 13(02), 1657-1670.
29. Fuzzy Logic Toolbox: User’s Guide for Use with MATLAB, The Math Works, Inc. Natick, MA, 1998.
30. MATLAB – Genetic Algorithm and Direct Search Toolbox: User’s Guide. The MathWorks, Inc., Natick, MA, 2004.
31. Júnior, M.P., da Silva, M.T., Guimarães, F.G., Euzébio, T.A.M. (2022). Energy savings in a rotary dryer due to a fuzzy multivariable control application. Dry. Technol. 40(6).
32. Nafisah N., Syamsiana I. N., Putri R. I., Kusuma W., Sumari A. D. W. (2024), Implementation of fuzzy logic control algorithm for temperature control in robusta rotary dryer coffee bean dryer. MethodsX 12, ISSN: 2215-0161.

Issue