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Speed control of doubly fed induction motor using backstepping control with interval type-2 fuzzy controller

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Języki publikacji
EN
Abstrakty
EN
The control of the doubly-fed induction motor is a complex operation because of this motor characterised by a non-linear multivariable dynamics, having settings that change over time and a significant link between the mechanical component and magnetic behavior (flux) (speed and couple). This article then proposes a new strategy of a robust control of this motor, which is decoupled due to the stator flux’s direction. The proposed control is integrated with the backstepping control which based on Lyapunov theory; this approach consists in constructively designing a control law of nonlinear systems by considering some state variables as being virtual commands, and the important branch of artificial intelligence type-2 fuzzy logic. The hybrid control backstepping-fuzzy logic consists in replacing the regulators applied to the backstepping control by regulators based on type-2 fuzzy logic. This control will be evaluated by numerous simulations where there is a parametric and non-parametric variation.
Czasopismo
Rocznik
Strony
art. no. 2023301
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Electrical Engineering Department, Faculty of Technology, University Mohamed Boudiaf of M’sila, Algeria
  • LGE Research Laboratory of M’sila, Algeria
  • Electrical Engineering Department, Faculty of Technology, University Mohamed Boudiaf of M’sila, Algeria
  • LGE Research Laboratory of M’sila, Algeria
  • Electrical Engineering Department, Faculty of Technology, University Mohamed Boudiaf of M’sila, Algeria
Bibliografia
  • 1. Vicatos MS, Tegopoulos AJ. A doubly-fed induction machine differential drive model for automobiles. IEEE Transactions on Energy Conversion 2003; 18(2): 225-230. https://doi.org/10.1109/TEC.2003.811732.
  • 2. Holdsworth L, Wu XG, Ekanayake JB, Jenkins N. Comparison of fixed speed and doubly fed induction wind turbines during power system disturbances. IEE Proceedings - Generation, Transmission and Distribution 2003;150(3):343-352. https://doi.org/10.1049/ip-gtd:20030251.
  • 3. Herizi A, Bouguerra A, Zeghlache S, Rouabhi RHybrid type-2 fuzzy sliding mode control of a doubly-fed induction machine (DFIM). AMSE Journals-IETA Publication, Advances in Modelling and Analysis C 2019; 74(2-4): 37-46. https://doi.org/10.18280/ama_c.742-401.
  • 4. El Ouanjli N, Motahhir S, Derouich A, El Ghzizal A, Chebabhi A, Taoussi M. Improved DTC strategy of doubly fed induction motor using fuzzy logic controller. Energy Reports 2019; 5: 271-279. https://doi.org/10.1016/j.egyr.2019.02.001.
  • 5. Leonhard W. Control of electrical drives. SpringerVerlag Berlin Heidelberg, 3rd edition, New York 2001.
  • 6. Pyrhonen J, Hrabovcova V, Semken RS. Electrical machine drives control: an introduction. John Wiley & Sons Ltd 2016, First edition.
  • 7. Li S, Yu X, Fridman L, Man Z, Wang X. Advances in variable structure systems and sliding mode control: theory and applications. Springer International Publishing AG 2018.
  • 8. Derbel N, Ghommam J, Zhu Q. Applications of sliding mode control. Springer Science+Business Media Singapore 2017.
  • 9. Krstic M, Kanellakopoulos I, Kokotovic PV. Nonlinear and adaptive control design. John Wiley & Sons, Inc. 1995.
  • 10. Kokotovic PV. The joy of feedback: nonlinear and adaptive. IEEE Control systems Magazine 1992; 12(3): 7-17. https://doi.org/10.1109/37.165507.
  • 11. Chaouch S, Herizi A, Serrai H, Nait Said M. Lyapunov and backstepping control design of induction motor system. 4th International Multi-Conference on Systems, Signals & Devices, Hammamet, Tunisia 2007.
  • 12. El Azzaoui M, Mahmoudi H, Ed-dahmani C. Backstepping control of a doubly fed induction generator integrated to wind power system. International Conference on Electrical and Information Technologies, Tangier, Morocco 2016.
  • 13. Bounar N, Boulkroune A, Boudjema F, M’Saad M, Farza M. Adaptive fuzzy vector control for a doublyfed induction motor. Neurocomputing 2015;151(2): 756-769. https://doi.org/10.1016/j.neucom.2014.10.026.
  • 14. Dualibe C, Verleysen M. Jespers PGA. Design of analog fuzzy logic controllers in CMOS technologies. Kluwer Academic Publishers, New York 2023.
  • 15. Lowen R, Verschoren A. Foundations of generic optimization. Volume 2: Applications of fuzzy control, genetic algorithms and neural networks. Springer 2008.
  • 16. Loukal K, Benalia L. Type-2 fuzzy logic control of a doubly-fed induction machine (DFIM). IAES International Journal of Artificial Intelligence (IJ-AI) 2015;4(4):139-152. https://doi.org/10.11591/ijai.v4.i4.pp139-152.
  • 17. Herizi A, Bouguerra A, Zeghlache S, Rouabhi R. Backstepping control of a doubly-fed induction machine based on fuzzy controller. European Journal of Electrical Engineering 2018;20(5-6):645-657. http://dx.doi.org/10.3166/ejee.20.647-657.
  • 18. Herizi A, Rouabhi R, Zemmit A. Comparative study of the performance of a sliding, sliding-fuzzy type 1 and a sliding-fuzzy type 2 control of a permanent magnet synchronous machine. Przegląd Elektrotechniczny 2022;98(11):21-29. https://doi.org/10.15199/48.2022.11.03.
  • 19. Castillo O, Melin P. A review on the design and optimization of interval type-2 fuzzy controllers. Applied Soft Computing 2012;12(4):1267-1278. https://doi.org/10.1016/j.asoc.2011.12.010.
  • 20. Juan R, Castillo O, Melin P, Díaz AR. A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Information Sciences 2009; 179(13): 2175-2193. https://doi.org/10.1016/j.ins.2008.10.016.
  • 21. Castillo O, Marroquín RM, Melin P, Valdez F, Soria J. Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Information Sciences 2012;192:19-38. https://doi.org/10.1016/j.ins.2010.02.022.
  • 22. Liang Q, Mendel JM. Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems 2000;8(5):535-550. https://doi.org/10.1109/91.873577.
  • 23. Mendel JM, John RI, Liu F. Interval type-2 fuzzy logic systems made simple. IEEE Transactions on Fuzzy Systems 2006;14(6):808-818. https://doi.org/10.1109/TFUZZ.2006.879986.
  • 24. Mendel J. Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice-Hall 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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