Fuzzy based supervision approach in the event of rotational speed inversion in an induction motor

• Autorzy: Hatem=Bellahsene Noura Rezika

Identyfikatory

DOI

10.2478/ama-2024-0009

• Języki publikacji: EN

Abstrakty

EN

This article aims to implement the fuzzy control for an asynchronous motor after a general representation of the vector control. We develop MAMDANI type fuzzy algorithm for MAS speed regulation; it’s one purpose is to cancel static error, decrease overshoot, decrease response time, and rise time to obtain an adequate response of the process and regulation and to have a precise, fast, stable and robust system. This paper investigates the design of a fuzzy-based approach for monitoring the inversion of the rotational speed of an induction motor. We will indeed present a robust vector control technique ex-tended to blur in the event of a fault. Direct torque control is known to produce fast and robust response in the AC drive system. However, in a steady state, a rapid and unexpected change in speed can occur which could be dangerous. The performance of the conventional PID controller can be improved by implementing fuzzy logic techniques. The first step is the modelling of the whole system, including the capacitors, the induction generator and the loads. The model is obtained using the Park transformation. The results are thus compared with those of the standard PID control. This approach is applied to a three-phase asynchronous motor (LS90Lz). The presented study improves the transient response time and the precision of the servo system. An inversion of the reference speed of rotation is considered, and the results are very convincing.

Słowa kluczowe

EN

fuzzy control; PID control; Park transformation; speed reversal; vector control; induction motor

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2024
• Tom: Vol. 18, no. 1
• Strony: 68--76
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Hatem=Bellahsene Noura Rezika

• LTII Laboratory, Department of Automatic, Telecommunications and Electronic, University A. Mira of Bejaia, Bejaia, Algeria

• https://orcid.org/0000-0002-6644-9930

Bibliografia

• 1. Trzynadlowski AM. Control of Induction Motors. Academic Press. 2001; ISBN 9780127015101; Nevada. https://doi.org/10.1016/B978-012701510-1/50000-3
• 2. PREMKUMAR K, THAMIZHSELVAN T, PRIY M, Vishnu et al. Fuzzy anti-windup pid controlled induction motor. International Journal of Engineering and Advanced Technology. 2019; 9(1): 184-189.
• 3. Korbut M, Szpica D. A Review of Compressed Air Engine in the Vehicle Propulsion System. Acta Mechanica et Automatica. 2021;15(4): 215-226. https://doi.org/10.2478/ama-2021-0028
• 4. https://www.researchgate.net/profile/Drkmkumar/publication/337285022_Fuzzy_Anti-indup_PID_Controlled_Induction_Motor/
• 5. Mehidi IM, SAAD N, MAGZOUB M and al. Simulation analysis and experimental evaluation of improved field-oriented controlled induc-tion motors incorporating intelligent controllers. IEEE Access. 2022;10: 18380-18394 Design and Simulation of Neuro-Fuzzy Con-troller for Indirect Vector-Controlled Induction Motor Drive SpringerLink.
• 6. Roose A, Yahya S, and Al-Rizzo H Fuzzy-logic control of an inverted pendulum on a cart. Computers & Electrical Engineering, Elsevier. 2017.
• 7. Badr B, Eltamaly A M, and Alolah. Fuzzy controller for three phases induction motor drives. IEEE Vehicle Power and Propulsion Confer-ence, Sept. 2010. https://doi.org/10.1109/VPPC.2010.5729080
• 8. Achbi M, Kehida S, Mhamd Lan and Hedi D A Neural-Fuzzy Ap-proach for Fault Diagnosis of Hybrid Dynamical Systems: Demon-stration on Three-Tank System" Acta Mechanica et Automatica. 2021; 15 (1): 1-8. https://doi.org/10.2478/ama-2021-0001
• 9. Khan F, Sulaimanandand and Ahmad Z Review of Switched Flux Wound-Field Machines Technology. IETE Technical Review, 2016.
• 10. Zerdali E, Met A, Barkak A and Erkan M Computationally efficient predictive torque control strategies without weighting factors Turkish Journal of Electrical Engineering and Computer Sciences.2022; 30 (6). https://doi.org/10.55730/1300-0632.3955
• 11. Ananthamoorthy N and Baskaran K. Velocity and torque control of permanent magnet synchronous motor using hybrid fuzzy propor-tional plus integral controlle. Journal of Vibration and Control, SAGE Publications. 2013; 20–29.
• 12. Aissaoui A, Abid M. A fuzzy logic controller for synchronous machine. Journal of Electrical Engineering. 2007; 285–290.
• 13. Bharathi Y and. al., “Multi-input fuzzy logic controller for brushless DC motor drives”, Defence Science Journal. 2008; 58 (1): 147–158. https://doi.org/10.14429/dsj.58.1632
• 14. Faiz J, Manoochehri M, Shahgholia G. Performance improvement of a linear permanent magnet synchronous drive using fuzzy logic con-troller. Proceedings of IEEE International Conference on Power Sys-tem Technology, Oct. 2010. https://doi.org/10.1109/POWERCON.2010.5666041
• 15. Soundarajan A, Sumathi A. Fuzzy based intelligent controller for power generating stations. Journal of Vibration and Control. 2011 (17): 214–227. https://doi.org/10.1177/1077546310371347
• 16. Ozturk N. Celik Educational Tool for the Genetic Algorithm-Based Fuzzy Logic Controller of a Permanent Magnet Synchronous Motor Drive. International Journal of Electrical Engineering Education. SAGE Publications. 2014. https://doi.org/10.7227/IJEEE.51.3.4
• 17. Bharathi Y and al. Multi-input fuzzy logic controller for brushless DC motor drives. Defence Science Journal. 2008; 58 (1): 147–158. https://doi.org/10.14429/dsj.58.1632
• 18. Ruiz J, Espinosa A, Romeral L. An introduction to fault diagnosis of permanent magnet synchronous machines in master’s degree cours-es. Comput. Appl. Eng. Educ. 2010; published online.
• 19. Siavashi E, Pahlavanhoseini R, Pejmanfar A. Using Clonal Selection Algorithm to optimize the Induction Motor Performance. Canadian Journal on Electrical and Electronics Engineering. 2011; 2 (9).
• 20. Zidani F, Nait Said R. Direct Torque Control of Induction Motor with Fuzzy Minimization Torque Ripple. Journal of Electrical Engineering. 2005; 56 (7–8): 183–188.
• 21. Ameur F. Application of Fuzzy Logic for a Ripple Reduction Strategy in DTC Scheme of a PWM Inverter fed Induction Motor Drives. Jour-nal of Electrical Systems. 2009; 1: 13–17.
• 22. Gadoue S, Giaouris D and Finch J. Artificial intelligence-based speed control of DTC induction motor drives: A comparative study. Electric Power Systems Research.2009; 79 (1): 210–219. https://doi.org/10.1016/j.epsr.2008.05.024
• 23. Liu S, Wang M, Chen Yand Li. SA Novel Fuzzy Direct Torque Control System for Three-level Inverter-fed Induction Machine .International Journal of Automation and Computing. 2020; 7 (1): 78–85. https://doi.org/10.1007/s11633-010-0078-7
• 24. Pasamontes M and al. Learning switching control: A tank level-control exercise,” IEEE Trans. Educ. 2012; 55 (2): 226–232. https://doi.org/10.1109/TE.2011.2162239
• 25. Guven U, Sonmez Yand Birogul SA. Computer based educational tool for fuzzy logic-controlled DC-DC converters. J. Polyt. 2007; 10: 339–346.