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Model Predictive Controlled IM Drive based on IT2FNN Controller

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Języki publikacji
EN
Abstrakty
EN
In this paper, the predictive torque control (PTC) based induction motor (IM) drive using an interval type-2 fuzzy neural network (IT2FNN) controller in the speed control loop is designed and tested in simulations. The states required for the proposed motor drive are estimated by extended complex Kalman filter (ECKF). The ECKF performs online estimations of stator currents, rotor fluxes, rotor mechanical speed, and rotor resistance. Compared to conventional extended Kalman filter (EKF), which estimates the same states/parameters, the designed ECKF has less computational burden because it does not contain matrix inverse and the matrix dimensions have been reduced. In addition, the rotor resistance estimated by ECKF is updated online to the PTC system. Thus, the performance of the PTC-based IM drive is improved against variations in the rotor resistance, whose value changes with operating conditions such as frequency and temperature. In order to force both the ECKF observer and the proposed IM drive, a challenging scenario containing the wide speed range operation of the IM is designed. Simulation results confirm the performance of the proposed speed-sensorless PTC-based drive that uses an IT2FNN controller in the speed control loop and the estimation performance of the ECKF observer.
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Rocznik
Strony
368--379
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • Department of Electrical and Electronics Engineering, Kayseri University, Kayseri, Türkiye
autor
  • Department of Electrical and Electronic Engineering, Nigde Omer Halisdemir University, Nigde, Türkiye
autor
  • Department of Electrical and Electronics Engineering, Kayseri University, Kayseri, Türkiye
Bibliografia
  • Abiyev, R. H. and Kaynak, O. (2010). Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants. IEEE Transactions on Industrial Electronics, 57(12), pp. 4147-4159. doi: 10.1109/TIE.2010.2043036.
  • Acikgoz, H. (2020). Real-Time Adaptive Speed Control of Vector-Controlled Induction Motor Drive Based on Online-Trained Type-2 Fuzzy Neural Network Controller. International Transactions on Electrical Energy Systems, 30(12), pp. 1-17. doi: 10.1002/2050-7038.12678.
  • Acikgoz, H., Coteli, R., Tanyildizi, E., Dandil, B. and Kayisli, K. (2023). Advanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified Golden Sine Algorithm. Electric Power Components and Systems, 51(10), pp. 933-948. doi: 10.1080/15325008.2023.2185838.
  • Alonge, F., D’Ippolito, F., Fagiolini, A. and Sferlazza, A. (2014). Extended Complex Kalman Filter for Sensorless Control of an Induction Motor. Control Engineering Practice, 27, pp. 1-10. doi: 10.1016/j. conengprac.2014.02.007.
  • Altinisik, Y. E. and Demir, R. (2021). EKF Based Model Predictive Torque Control of Induction Motors. European Journal Science and Technology, 38, pp. 858-863.
  • Ayodhya, A., Angadi, S. and Raju, A. B. (2022). Comparative analysis of PI and fuzzy based speed controllers for indirect field-oriented control of induction motor drives. In: 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, pp. 1-6.
  • Bednarz, S. A. and Dybkowski, M. (2019). Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator. Applied Sciences, 9(23), p. 5145. doi: 10.3390/app9235145.
  • Castro, J. R., Castillo, O., Melin, P. and Rodríguez-Díaz, A. (2009). A Hybrid Learning Algorithm for a Class of Interval Type-2 Fuzzy Neural Networks. Information Sciences Special Section on High Order Fuzzy Sets, 179(13), pp. 2175-2193.
  • Das, S., Kumar, R. and Pal, A. (2019). MRAS Based Speed Estimation of Induction Motor Drive Utilizing Machine’s d- and q- Circuit Impedances. IEEE Transactions on Industrial Electronics, 66(6), pp. 4286-4295. doi: 10.1109/TIE.2018.2860530.
  • Demir, R. (2023). Robust Stator Flux and Load Torque Estimations for Induction Motor Drives with EKF-Based Observer. Electrical Engineering, 105, pp. 551-562. doi: 10.1007/s00202-022-01717-y.
  • Elmorshedy, M. F., Xu, W., Ali, M. M., Liu, Y. and Allam, S. M. (2020). High performance speed sensorless finite-set predictive thrust control of a linear induction motor based on MRAS and fuzzy logic controller. In: 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), Nanjing, China, pp. 3039-3044.
  • Habibullah, M. and Lu, D. D. C. (2015). A Speed-Sensorless FS-PTC of Induction Motors Using Extended Kalman Filters. IEEE Transactions on Industrial Electronics, 62(11), pp. 6765-6778. doi: 10.1109/TIE.2015.2442525.
  • Kamalapur, G. and Aspalli, M. S. (2023). Direct Torque Control and Dynamic Performance of Induction Motor Using Fractional Order Fuzzy Logic Controller. International Journal of Electrical and Computer Engineering, 13(4), pp. 3805-3816. doi: 10.11591/ijece.v13i4.pp3805-3816.
  • Kayacan, E., Cigdem, O. and Kaynak, O. (2012). Sliding Mode Control Approach For Online Learning as Applied To Type-2 Fuzzy Neural Networks And Its Experimental Evaluation. EEE Transactions on Industrial Electronics, 59(9), pp. 3510–3520. doi: 10.1109/TIE.2011.2182017.
  • MathWorks Inc. (2023). Simulink. Simulation and Model-Based Design. MA, USA: Natick
  • Menaa, M., Touhami, O., Ibtiouen, R. and Fadel, M. (2008). Sensorless Direct Vector Control of an Induction Motor. Control Engineering Practice, 16(1), pp. 67-77. doi: 10.1016/j. conengprac.2007.04.002.
  • Mencou, S., Yakhlef, M. B. and Tazi, E. B. (2023). Fuzzy logic speed controller for robust direct torque control of induction motor drives. In: S. Motahhir, B. Bossoufi, eds., Digital Technologies and Applications, Lecture Notes in Networks and Systems. Cham: Springer Nature Switzerland, pp. 601-611.
  • Mousavi, M. S., Davari, S. A., Nekoukar, V., Garcia, C., He, L., Wang, F. and Rodriguez, J. (2023). Predictive Torque Control of Induction Motor Based on a Robust Integral Sliding Mode Observer. IEEE Transactions on Industrial Electronics, 70(3), pp. 2339-2350. doi: 10.1109/TIE.2022.3169831.
  • Mousavi, M. S., Davari, S. A., Nekoukar, V., Garcia, C. and Rodriguez, J. (2021). Finite-Set Model Predictive Current Control of Induction Motors by Direct Use of Total Disturbance. IEEE Access, 9, pp. 107779-107790. doi: 10.1109/ ACCESS.2021.3100506.
  • Orlowska-Kowalska, T., Korzonek, M. and Tarchala, G. (2019). Stability Improvement Methods of the Adaptive Full-Order Observer for Sensorless Induction Motor Drive-Comparative Study. EEE Transactions on Industrial Informatics, 15(11), pp. 6114-6126. doi: 10.1109/TII.2019.2930465.
  • Prasad, R. R. and Durgasukuamar, G. (2021). Performance Analysis of PI, T1NFC, and T2NFC of Indirect Vector Control-Based Induction Motor Using DSpace-2812. Journal Européen des Systèmes Automatisés, 54, pp. 671–682. doi: 10.18280/jesa.540502.
  • Rodriguez, J., Kennel, R. M., Espinoza, J. R., Trincado, M., Silva, C. A. and Rojas, C. A. (2012). High-Performance Control Strategies for Electrical Drives: An Experimental Assessment. EEE Transactions on Industrial Electronics, 59(2), pp. 812-820. doi: 10.1109/TIE.2011.2158778.
  • Sabzalian, M. H., Mohammadzadeh, A., Lin, S. and Zhang, W. (2020). A Robust Control of A Class of Induction Motors Using Rough Type-2 Fuzzy Neural Networks. Soft Computing, 24, pp. 9809-9819. doi: 10.1007/s00500-019-04493-3.
  • Wang, F., Zhang, Z., Mei, X., Rodríguez, J. and Kennel, R. (2018). Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control. Energies, 11(1), 120.
  • Yildiz, R., Barut, M. and Zerdali, E. (2020). A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors. IEEE Transactions on Industrial Informatics, 16(10), pp. 6423-6432. doi: 10.1109/TII.2020.2964876.
  • You, J., Wu, W. and Wang, Y. (2018). An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines. In: 2018 Annual American Control Conference (ACC), Milwaukee, WI, USA, pp. 307–312. doi: 10.23919/ ACC.2018.8431006.
  • Zerdali, E. (2018). The Design of Extended Complex Kalman Filter Based Speed Sensorless Induction Motor Drive. Gazi University Science Journal: Part: C ‘Design and Technology, 6(4), pp. 877-886.
  • Zerdali, E. and Demir, R. (2021). Speed-Sensorless Predictive Torque Controlled Induction Motor Drive with Feed-Forward Control of Load Torque for Electric Vehicle Applications. Turkish Journal of Electrical Engineering and Computer Sciences, 29(1), pp. 223-240. doi: 10.3906/elk-2005-75.
Uwagi
Special Section - Artificial Intelligent Based Designs and Applications for the Control of Electrical Drives
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e1f8206b-0daa-4799-a899-69ed9673628c
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