Abstract
The method of controlling the rectification process using fuzzy control based on fuzzy logic is studied. The structural scheme of the rectification process control system based on fuzzy logic is proposed. The analysis of the operation of the fuzzy rectifier is also carried out by comparing the human with the elements of fuzzy thinking. It is shown that the logical thinking of the human is similar to the functions performed by the fuzzy rectifier and the work of the components of the intellectual control system. The terms of linguistic variables are presented to reflect the rectification process of multi-component mixtures. The principle of the reference model control and the structural scheme of the fuzzy control system are given. Based on the obtained equations, expressions for the membership functions are written and an algorithm used to implement fuzzy control is proposed. To compare the performance of the fuzzy PID-controller control system and the traditional PID-controller control system, a structural scheme is constructed and transition process graphs are constructed and compared.
First Page
193
Last Page
199
References
1. Avazov Yu.Sh. Architecture of the Intellectual Control System of the Lifecycle of Technological Complexes for the Rectification of Multicomponent Mixtures // International scientific and technical journal “Chemical Technology. Control and Management”. -№2(104). -2022. –PP.52-58.
2. Avazov Yu.Sh. Intelligent Control System of the Lifecycle of Technological Complexes of Rectification Processes // International Journal of Discoveries and Innovations in Applied Sciences. e-ISSN: 2792-3983. www.openaccessjournals.eu. -Volume 2. -Issue 8. -Spain, 2022. –PP.45-48.
3. Avazov Yu.Sh. Determination of the cost and duration of the lifecycle of rectification columns for multi-component mixtures // International scientific and technical journal “Chemical Technology. Control and Management”. -№3(105). -2022. –PP.30-35.
4. Yusupbekov N.R., Avazov Yu.Sh. Method of intelligent control of the process of rectification of multicomponent mixtures // Journal “Technical sciences and innovation”. -№3(13). -2022. –PP.148-154.
5. Avazov Yu.Sh. Governance Model the Stochastic Process of Rectification of Multicomponent Mixtures Based on Fuzzy Logic // Journal “Advances in Intelligent Systems and Computing”. Switzerland: Springer Nature. 2021. Volume 1323. –PP.364-376. (2021). https://doi.org/10.1007/978-3-030-68004-6_48
6. Avazov Yu., Shodiev M., Rajabov A., Turaev Kh. Control of the lifecycle of a technological complex for the rectification of multicomponent mixtures under conditions of parameter uncertainty. E3S Web of Conferences, 25 August 2023, 10.1051/e3sconf/202341705010
7. Zimmermann, G. What makes systems intelligent. Discov Psychol 4, 127 (2024). https://doi.org/10.1007/s44202-024-00245-z
8. Rumovskaya, S.B. Open Intelligent Systems: The Concept and Approaches to Development. Pattern Recognit. Image Anal. 34, 724–730 (2024). https://doi.org/10.1134/S1054661824700585
9. I.S. Konstantinov, A. G. Filatov, and Y. V. Kasyanov, “Principles of constructing intelligent automated control systems with fuzzy regulation based on logical-linguistic models of knowledge representation,” in Collection of Proc. Seventh Academic Readings of the RAASN Modern Problems of Construction Materials Science (Belgorod, 2001), pp. 154–158.
10. Ma, H., Wang, D., Ren, J. et al. Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization. Nonlinear Dyn 113, 583–595 (2025). https://doi.org/10.1007/s11071-024-10173-1
11. Rawat, D., Gupta, M.K. & Sharma, A. Intelligent control of robotic manipulators: a comprehensive review. Spat. Inf. Res. 31, 345–357 (2023). https://doi.org/10.1007/s41324-022-00500-2
12. Zhu, M., Liang, M., Li, H. et al. Intelligent acceptance systems for distribution automation terminals: an overview of edge computing technologies and applications. J Cloud Comp 12, 149 (2023). https://doi.org/10.1186/s13677-023-00529-0
13. Rybina, G.V., Slinkov, A.A. & Belov, D.D. Intelligent Technology for Construction of Dynamic Integrated Expert Systems: Specificities of Construction of Simulation Models of the External Environment. Pattern Recognit. Image Anal. 34, 731–737 (2024). https://doi.org/10.1134/S1054661824700597
14. Chinnasamy, P., Samrin, R., Sujitha, B.B. et al. Integrating Intelligent Breach Detection System into 6 g Enabled Smart Grid-Based Cyber Physical Systems. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11192-2
15. Xu, Q., Zhou, G., Zhang, C. et al. Generative AI and DT integrated intelligent process planning: a conceptual framework. Int J Adv Manuf Technol 133, 2461–2485 (2024). https://doi.org/10.1007/s00170-024-13861-9
16. Liu S, Bao J, Zheng P (2023) A review of digital twin-driven machining: from digitization to intellectualization. J Manuf Syst 67:361–378. https://doi.org/10.1016/j.jmsy.2023.02.010
17. Zhang C, Zhou G, Hu J, Li J (2020) Deep learning-enabled intelligent process planning for digital twin manufacturing cell. Knowl Based Syst 191:105247. https://doi.org/10.1016/j.knosys.2019.105247
18. Lu Y, Xu X, Wang L (2020) Smart manufacturing process and system automation – a critical review of the standards and envisioned scenarios. J Manuf Syst 56:312–325. https://doi.org/10.1016/j.jmsy.2020.06.010
19. Yusupbekov, N., Abdurasulov, F., Adilov, F., Ivanyan, A. Concepts and Methods of “Digital Twins” Models Creation in Industrial Asset Performance Management Systems. Advances in Intelligent Systems and Computing, 2021, 1197 AISC.рр.1589–1595. (2021).
20. B. He, K.-J. Bai. Digital twin-based sustainable intelligent manufacturing: a review. Adv Manuf (2019), pp. 1-21.
21. Y. Lu, C. Liu, I. Kevin, K. Wang, H. Huang, X. Xu. Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robot Comput Integr Manuf, 61 (2020), p. 101837.
22. S. Chakraborty, S. Adhikari. Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures. Volume 243, 2021. 106410. ISSN 0045-7949. doi.org/10.1016/j.compstruc.2020.106410.
23. Li M, Wang R, Zhou X, Zhu Z, Wen Y, Tan R (2023) ChatTwin: toward automated digital twin generation for data center via large language models. In: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. ACM, Istanbul Turkey, pp 208–211. https://doi.org/10.1145/3600100.3623719
24. Yang H, Siew M, Joe-Wong C (2024) An LLM-based digital twin for optimizing human-in-the loop systems. arXiv preprint arXiv:2403.16809. http://arxiv.org/abs/2403.16809.
Recommended Citation
Avazov, Yusuf Shodievich and Abdullaeva, Kamola Rustamovna
(2024)
"IMPLEMENTATION OF THE METHOD OF FUZZY CONTROL OF THE RECTIFICATION PROCESS BASED ON FUZZY LOGIC,"
Chemical Technology, Control and Management: Vol. 2024:
Iss.
5, Article 33.
DOI: https://doi.org/10.59048/2181-1105.1653