COMMANDE HYBRIDE DE MODE COULISSANT FUZZY TYPE-1 ET 2 DU MOTEUR À INDUCTION
DOI :
https://doi.org/10.59277/RRST-EE.2024.2.5Mots-clés :
Étude comparative, Logique floue, Commande hybride, Moteur à induction, La modélisation, mode glissantRésumé
Cet article se concentre sur le développement de deux techniques innovantes de contrôle de moteurs à induction (IM). Ces techniques reposent sur l’hybridation de la théorie de Lyapunov (mode glissant) et de l’intelligence artificielle (logique floue de type 1 et type 2). Nous comparerons ensuite ces deux techniques de contrôle pour déterminer laquelle est la plus robuste. Cette analyse comparative s'appuiera sur une série de tests que nous avons réalisés, couvrant le fonctionnement transitoire et stationnaire du système dans des conditions identiques. Le premier test consiste à observer les résultats de simulation obtenus en appliquant ces techniques de contrôle au moteur pour contrôler la puissance mécanique générée. Cette comparaison qualitative permet d'évaluer ces contrôles avec et sans application de variations externes. Le deuxième test quantifie les différentes lois de contrôle à partir de mesures quantifiées, mettant en avant les performances de chaque technique en termes d'erreur et de temps. Ce test est appelé comparaison quantitative. Enfin, le dernier examen consiste à modifier les paramètres de la machine, car ces valeurs subissent naturellement des fluctuations provoquées par divers phénomènes physiques comme la saturation des inductances et l'échauffement des résistances. Cette comparaison permet d'évaluer la robustesse des techniques de contrôle.
Références
(1) S. Lekhchine, T. Bahi, I. Abadlia, H. Bouzeria, PV-battery energy storage system operating of asynchronous motor driven by using fuzzy sliding mode control, International Journal of Hydrogen Energy, 42, pp. 8756–8764 (2017).
(2) A. Cavagnino, Asynchronous Motors, Encyclopedia of Electrical and Electronic Power Engineering, pp. 280–298 (2023).
(3) J. Cheng, Y. Xiong, Application of teaching-learning-based optimization algorithm in rotor fault diagnosis for asynchronous motor, Procedia Computer Science, 131, pp. 1275–1281 (2018).
(4) S. Nassiri, M. Labbadi, M. Cherkaoui, Optimal integral super-twisting sliding-mode control for high efficiency of pumping systems, IFAC-Papers Online, 55, 12, pp. 234–239 (2022).
(5) I. Sami, S. Ullah, Integral super twisting sliding mode based sensorless predictive torque control of induction motor, Power Syst., 8, pp. 186740–186755 (2020).
(6) J. Song et al., Asynchronous sliding mode control of singularly perturbed semi-Markovian jump systems: application to an operational amplifier circuit, Automatica, 108, pp. 1–8 (2020).
(7) M. Errouha, A. Derouich et al., Optimization and control of water pumping PV systems using fuzzy logic controller, Energy Rep., 5, pp. 853–865 (2019).
(8) S. El Daoudi, L. Lazrak, N. El Ouanjli, M. Ait Lafkih. Sensorless fuzzy direct torque control of induction motor with sliding mode speed controller, Computers & Electrical Engineering, 96, 107490 (2021).
(9) T. Ramesh, A.K. Panda, S.S. Kumar, Type-2 fuzzy logic control-based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive, ISA Transactions, 57, pp. 262–275 (2015).
(10) M.S. Adouairi, B. Bossoufi, S. Motahhir, I. Saady, Application of fuzzy sliding mode control on a single-stage grid-connected PV system based on the voltage-oriented control strategy, Results in Engineering, 17, pp. 1–9 (2023).
(11) R. Kavikumar et al., Sliding mode control for IT2 fuzzy semi-Markov systems with faults and disturbances, Applied Mathematics and Computation, 423, 127028 (2022).
(12) M. Mokhtari et al., Sliding mode & single input fuzzy logic controllers for voltage regulation of an asynchronous wind turbine using STACOM, IFAC Papers online, 53-2, 22, pp. 12803–12808 (2020).
(13) D. Zellouma, Y. Bekakra, H. Benbouhenni, Field-oriented control based on parallel proportional–integral controllers of induction motor drive, Energy Rep., 9, pp. 4846–4860 (2023).
(14) F. Zhao et al., The effects of parameter variations on the torque control of induction motor, CAA International Conference on Vehicular Control and Intelligence (CVCI), Hangzhou, China (2020).
(15) H. He et al., research on active disturbance rejection control of induction motor, IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC 2019).
(16) A. Ranjbar N, H.A. Kholerdi. Chaotification and fuzzy PI control of three-phase induction machine using synchronization approach, Chaos, Solitons and Fractals, 91, pp. 443–451 (2016).
(17) T. Wang, Y. Wang, Z. Zhang, Z. Li, C. Hu, F. Wang, Comparison and analysis of predictive control of induction motor without weighting factors, 2nd International Joint Conference on Energy and Environmental Engineering (CoEEE 22) (2022).
(18) A. Benzouaoui, H. Khouidmi, B. Bessedik, Parallel model predictive direct power control of DFIG for wind energy conversion, Int. J. Electr. Power. Energy Syst., 125, pp. 1–12 (2021).
(19) O. Zamzoum, A. Derouich et al., Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation, Journal of Cleaner Production, 265, pp. 1–21 (2020).
(20) R. Rouabhi. R. Abdessemed. A. Chouder. A. Djerioui, Power quality enhancement of grid-connected doubly-fed induction generator using sliding mode control, International Review of Electrical Engineering, 10, pp. 266–276 (2015).
(21) H. Acikgoz et al., Dc-link voltage control of three-phase PWM rectifier by using artificial bee colony-based type-2 fuzzy neural network, Microprocessors and Microsystems, 78, pp. 1–13 (2020).
(22) J. Yang, N. Meng, Multi-loop power control strategy of current source PWM rectifier, Energy Rep., 8, pp. 11675–11682 (2022).
(23) L. Kou et al., Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features, ISA Transactions, 101, pp. 399-407 (2020).
(24) Y. Li, D. Wang, Servo motor sliding mode control based on fuzzy power index method, Computers & Electrical Engineering, 94, 107351 (2021).
(25) M.M. Lumertz, S.T.C.A. dos Santos, P.R.U. Guazzelli, C.M.R. de Oliveira, M.L. de Aguiar, J.R.B.A. Monteiro, Performance-based design of pseudo-sliding mode speed control for electrical motor drives, Control Engineering Practice, 132, 105413 (2023).
(26) M.M. Zirkohi, Fast terminal sliding mode control design for position control of induction motors using adaptive quantum neural networks, Applied Soft Computing, 115, 108268 (2022).
(27) Z. Yang, Q. Ding, X. Sun, C. Lu, H. Zhu, Speed sensorless control of a bearingless induction motor based on sliding mode observer and phase-locked loop, ISA Transactions, 123, pp. 346-356 (2022).
(28) Z. Yang, Q. Ding, X. Sun, H. Zhu, C. Lu, Fractional-order sliding mode control for a bearingless induction motor based on improved load torque observer, Journal of the Franklin Institute, 358, pp. 3701–3725 (2021).
(29) Y. Mousavi, G. Bevan, K. Beklan, A. Fekih, Sliding mode control of wind energy conversion systems: trends and applications, Renew Sustain. Energy Rev., 167, pp. 1–28 (2022).
(30) K. Roummani et al., A new concept in direct-driven vertical axis wind energy conversion system under real wind speed with robust stator power control, Renew. Energy, 143, pp. 478-487 (2019).
(31) A. Mechernene, M. Loucif, M. Zerikat, Induction motor control based on a fuzzy sliding mode approach, Rev. Roum. Sci. Techn.–Électrotechn. et Énerg., 64, 1, pp. 39–44 (2019).
(32) K. Makhloufi et al., Adaptive neuro-fuzzy-slip control of a linear synchronous machine, Rev. Roum. Sci. Techn.–Électrotechn. et Énerg. 67, 4, pp. 425–431 (2022).
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(c) Copyright REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 2024
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