Sabit mıknatıslı senkron motorun kapalı çevrim V/f hız kontrolü için nötrosofik değerli PID denetleyicisi

Öz

Bu makalede, Matlab'da bir bulanık çıkarım sistemi ve bir nötrosofik bulanık mantık denetleyici (NFLC) oluşturularak nötrosofik mantık tabanlı kapalı döngü V/f kontrol sistemi kurulmuştur. NFLC, belirsizlik ve kesin olmayan verileri hesaba katarak kontrol yapabilmesine rağmen, bazen hızlı ve doğru yanıtlar gerektiren uygulamalarda yetersiz kalabilir. Bu nedenle sabit mıknatıslı senkron motor (PMSM) ile bir Simulink ortamında test etmek için NFLC-PID blok diyagramı oluşturulmuştur. NFLC-PID, NFLC’nin sağladığı kesin olmayan verileri de dikkate alarak daha doğru bir geri bildirim yapar. Böylece, sistemin kontrol performansı daha da geliştirilmiştir. Simülasyon sonuçları, NFLC-PID’in PMSM'yi verimli ve başarılı bir şekilde yönettiğini göstermektedir.

Anahtar Kelimeler

• Sabit mıknatıslı senkron motor
• Bulanık mantık denetleyicisi
• Nötrosofik bulanık mantık denetleyicisi

Neutrosophic valued PID controller for closed loop V/f speed control of permanent magnet synchronous motor

Abstract

In this article, a neutrophic logic-based closed loop V/f control system has been established by creating a fuzzy inference system and a neutrophic fuzzy logic controller (NFLC) in Matlab. Although NFLC can control by taking uncertainty and imprecise data into account, it can sometimes fall short in applications that require fast and accurate responses. Therefore, the NFLC-PID block diagram has been established to test in a Simulink environment with permanent magnet synchronous motor (PMSM). The NFLC-PID takes into account the imprecise data provided by the NFLC and provides a more accurate feedback. Thus, the control performance of the system is further improved. The simulation results demonstrate that the NFLC-PID managed the PMSM efficiently and successfully.

Keywords

• Permanent magnet synchronous motor
• Fuzzy logic controller
• Neutrosophic fuzzy logic controller

Kaynakça

1. L. Zadeh, Fuzzy sets, Information Control, 8, 338–353, 1965. https://doi.org/10.1016/s0019-9958(65)90241-x
2. I. Bloch, Fuzzy sets for image processing and understanding, Fuzzy Sets and Systems, 281, 280-291, 2015. https://doi.org/10.1016/j.fss.2015.06.017
3. J. M. Sánchez-Lozano, J. Serna, and A. Dolón-Payán, Evaluating military training aircrafts through the combination of multi-criteria decision-making processes with fuzzy logic, A case study in the Spanish Air Force Academy. Aerospace Science and Technology, 42, 58-65, 2015. https://doi.org/10.1016/j.ast.2014.12.028
4. I. Rodríguez-Fdez, M. Mucientes, and A. Bugarín, Learning fuzzy controllers in mobile robotics with embedded preprocessing, Applied Soft Computing, 26, 123-142, 2015. https://doi.org/10.1016/ j.asoc.2014.09.021
5. K. Premkumar, and B.V. Manikandan, Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor, Neurocomputing, 157, 76-90, 2015. https://doi.org/10.1016/j.neucom.2015.01.032
6. M. El-Bardini, and A.M. El-Nagar, Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system, ISA transactions, 53(3), 732-743, 2014. https://doi.org/10.1016/j.isatra.2014.02.007
7. J. Zhao, and B.K. Bose, Evaluation of membership functions for fuzzy logic-controlled induction motor drive, In IEEE 2002 28th Annual Conference of the Industrial Electronics Society, 1, 229-234, 2002. https://doi.org/10.1109/iecon.2002.1187512
8. O.A.M. Ali, A.Y. Ali, and B.S. Sumait, Comparison between the effects of different types of membership functions on fuzzy logic controller performance, International Journal, 76, 76-83, 2015. https://doi.org/10.1016/j.fss.2014.04.006

9. E. Çelik, A. Dalcali, N. Öztürk, and R. Canbaz, An adaptive PI controller schema based on fuzzy logic controller for speed control of permanent magnet synchronous motors, In 4th International Conference on Power Engineering, Energy and Electrical Drives, IEEE, 715-720, 2013. https://doi.org/10.1109/ powereng.2013.6635698
10. F. Smarandache, Definiton of neutrosophic logic-a generalization of the intuitionistic fuzzy logic, In EUSFLAT Conf., 141-146, 2003. https://doi.org/ 10.2139/ssrn.2721587
11. V. Gomathy, T. Jayasankar, M. Rajaram, E.A. Devi, and S. Priyadharshini, Optimal neutrosophic rules based feature extraction for data classification using deep learning model, In Soft Computing for Data Analytics, Classification Model, and Control, Springer, Cham., 57-79, 2022. https://doi.org/ 10.1007/978-3-030-92026-5_4
12. M. Saqlain, and M. Saeed, Fuzzy logic controller for aviation parking with 5G communication technology, In Intelligent and Fuzzy Techniques in Aviation 4.0 Springer, Cham., 41-62, 2022. https://doi.org/ 10.1007/978-3-030-75067-1_3
13. A.Q. Ansari, R. Biswas, and S. Aggarwal, Extension to fuzzy logic representation: Moving towards neutrosophic logic-A new laboratory rat, In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8, 2013. https://doi.org/10.1109/ fuzz-ieee.2013.6622412
14. M.S. Can, and Ö.F. Özgüven, Design of the neutrosophic membership valued fuzzy-PID controller and rotation angle control of a permanent magnet direct current motor, Journal of New Results in Science, 5, 126-138, 2016. https://doi.org/ 10.28948/ngumuh.1258587
15. S. Sondhi, and Y.V. Hote, Fractional order PID controller for load frequency control, Energy Conversion and Management, 85, 343-353, 2014. https://doi.org/10.1016/j.enconman.2014.05.091
16. C. Mitsantisuk, M. Nandayapa, K. Ohishi, and S. Katsura, Design for sensorless force control of flexible robot by using resonance ratio control based on coefficient diagram method”, Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 54(1), 62-73, 2013. https://doi.org/ 10.7305/automatika.54-1.311
17. K. S. Tang, K. F. Man, G. Chen, and S. Kwong, An optimal fuzzy PID controller, IEEE transactions on industrial electronics, 48(4), 757-765, 2001. https://doi.org/10.1109/41.937407
18. V.S. Amit, and K.R. Rajagopal, PM synchronous motor speed control using hybrid fuzzy- PI with novel switching functions, IEEE Transaction on Magnetics, 45(10), 4672-4675, 2009. https://doi.org/ 10.1109/tmag.2011.2159831
19. S. Li, and Z. Liu, Adaptive speed control for permanent magnet synchronous motor system with variations of load inertia, IEEE Transactions on Industrial Electronics, 56(8), 3050-3059, 2009. https://doi.org/10.1109/tie.2009.2024655
20. R.H. Du, Y.F. Wu, W. Chen, and Q. Chen, Adaptive fuzzy speed control for permanent magnet synchronous motor servo systems, Electric Power Components and Systems, 42(8), 798-807, 2014. https://doi.org/10.1080/15325008.2014.893546
21. L. Huixian, and L. Dhihua, Speed control of PMSM servo system using predictive functional control and extended state observer, IEEE Transactions on Industrial Electronics, 59(2), 1171-1183, 2012. https://doi.org/10.6113/jpe.2014.14.3.549
22. A. M. O. Anwer, F. A. Omar, and A. A. Kulaksiz, Design of a fuzzy logic-based MPPT controller for a PV system employing sensorless control of MRAS-based PMSM, International Journal of Control, Automation and Systems, 18(11), 2788-2797, 2020. https://doi.org/10.1007/s12555-019-0512-8
23. G. Haçat, Y. Çetinceviz, and E. Şehirli, Speed control of BLDC by using neutrosophic fuzzy logic controller, In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU) IEEE, 2021. https://doi.org/10.1109/asyu52992.2021.9599021
24. M. Zhang, L. Zhang, and H.D. Cheng, A neutrosophic approach to image segmentation based on watershed method, Signal Processing, 90(5), 1510-1517, 2010. https://doi.org/10.1016/j.sigpro.2009.10.021
25. F. Smarandache, Collected Papers III. Oradea, 2000. https://doi.org/10.1007/978-1-4612-2116-6
26. H. Wang, F. Smarandache, Y.Q. Zhang, and R. Sunderraman, Single valued neutrosophic sets, Multispace and Multistructure, 4, 410-413, 2010. https://doi.org/10.1109/grc.2006.1635801
27. M. Arora, and U.S. Pandey, Generalization of functional dependencies in total neutrosophic relation, Infinite Study, 2012. https://doi.org/10.2118/155211-ms
28. A. Elhangari, Sliding-mode control of the permanent magnet synchronous motor (PMSM), The Degree Master of Science in Electrical Engineering, The School of Engineering of the University of Dayton, United State, 2013. https://doi.org/10.1109/ iccee.2008.170
29. Y. Omur, Nonlinear system control with adaptive fuzzy controller, Ege University Institute of Science Master Thesis, İzmir, 2009. https://doi.org/ 10.1109/chicc.2006.4347123
30. N. Baykal, and T. Beyan, Fuzzy logic: expert systems and controllers, Bıçaklar Kitabevi, 2004. https://doi.org/10.1002/0471698504.ch3
31. K.M. Passino, S. Yurkovich, and M. Reinfrank, Fuzzy control, Citeseer, 1998. https://doi.org/10.1109/acc.1994.751801