Adaptive RBFNN strategy for fault tolerant control: application to DSIM under broken rotor bars fault

Автор: Noureddine Layadi, Samir Zeghlache, Ali Djerioui, Hemza Mekki, Fouad Berrabah

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 2 vol.11, 2019 года.

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This paper presents a fault tolerant control (FTC) based on Radial Base Function Neural Network (RBFNN) using an adaptive control law for double star induction machine (DSIM) under broken rotor bars (BRB) fault in a squirrel-cage in order to improve its reliability and availability. The proposed FTC is designed to compensate for the default effect by maintaining acceptable performance in case of BRB. The sufficient condition for the stability of the closed-loop system in faulty operation is analyzed and verified using Lyapunov theory. To proof the performance and effectiveness of the proposed FTC, a comparative study within sliding mode control (SMC) is carried out. Obtained results show that the proposed FTC has a better robustness against the BRB fault.

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Double star induction machine, Radial base function neural network, Sliding mode control, Robustness, Fault tolerant control, Broken rotor bars

Короткий адрес: https://readera.ru/15016572

IDR: 15016572   |   DOI: 10.5815/ijisa.2019.02.06

Список литературы Adaptive RBFNN strategy for fault tolerant control: application to DSIM under broken rotor bars fault

  • N. Layadi, S. Zeghlache, T. Benslimane and F. Berrabah, “Comparative Analysis between the Rotor Flux Oriented Control and Backstepping Control of a Double Star Induction Machine (DSIM) under Open-Phase Fault,” AMSE Journals, Series Advances C, vol. 72, N 4, pp. 292–311, 2018.
  • H. Rahali, S. Zeghlache and L. Benalia, “Adaptive field-oriented control using supervisory type-2 fuzzy control for dual star induction machine,” International Journal of Intelligent Engineering and Systems, vol. 10, N° 4, pp. 28-40, 2017.
  • Z. Tir, Y. Soufi, M. N. Hashemnia, O. P. Malik and K. Marouani, “Fuzzy logic field oriented control of double star induction motor drive,” Electrical Engineering, vol. 99, N 2, pp. 495-503, 2017.
  • M. Abd-El-Malek, A. K. Abdelsalam and O. E. Hassan,“ Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform,” Mechanical Systems and Signal Processing, vol. 93, pp. 332-350, 2017.
  • R. A. Lizarraga-Morales, C. Rodriguez-Donate, E. Cabal-Yepez, M. Lopez-Ramirez, L. M. Ledesma-Carrillo and E. R. Ferrucho-Alvarez, “Novel FPGA-based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 66, N 7, pp. 1760-1769.
  • E. Elbouchikhi, V. Choqueuse, F. Auger and M. E. H. Benbouzid, “Motor Current Signal Analysis Based on a Matched Subspace Detector,” IEEE Transactions on Instrumentation and Measurement., vol. 66, N 12, pp. 3260-3270, 2017.
  • Z. Hou, J. Huang, H. Liu, T. Wang and L. Zhao, ”Quantitative broken rotor bar fault detection for closed-loop controlled induction motors,” IET Electric Power Applications, vol. 10, N° 5, pp. 403-410, 2016.
  • H. Yang and J. Liu, “An adaptive RBF neural network control method for a class of nonlinear systems,” IEEE/CAA Journal of Automatica Sinica, vol. 5, N 2, pp. 457-462, 2018.
  • L. Zhu, Z. Wang, Y. Zhou and Y. Liu, “Adaptive Neural Network Saturated Control for MDF Continuous Hot Pressing Hydraulic System With Uncertainties,” IEEE Access, vol. 6, pp. 2266-2273, 2018.
  • J. Zhi, Y. Chen, X. Dong, Z. Liu and C. Shi, “Robust adaptive FTC allocation for over-actuated systems with uncertainties and unknown actuator non-linearity,” IET Control Theory & Applications, vol. 12, N 2, pp. 273-281, 2017.
  • S. Zeghlache, H. Mekki, A. Bouguerra and A. Djerioui, ”Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV,” ISA transactions, vol. 60, pp. 267-278, 2018.
  • J. Listwan and K. Pieńkowski, ”Sliding-mode direct field-oriented control of six-phase induction motor,” Czasopismo Techniczne, vol. (2-M), pp. 95-108, 2016.
  • M. A. Fnaiech, F. Betin, G. A. Capolino and F. Fnaiech, “Fuzzy logic and sliding-mode controls applied to six-phase induction machine with open phases,” IEEE Transactions on Industrial Electronics, vol. 57, N° 1, pp. 354-364, 2010.
  • M. Manohar and S. Das, ”Current sensor fault-tolerant control for direct torque control of induction motor drive using flux-linkage observer,” IEEE Transactions on Industrial Informatics, vol. 13, N 6, pp. 2824-2833, 2017.
  • H. Mekki, O. Benzineb, D. Boukhetala, M. Tadjine and M. Benbouzid, ”Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems,” ISA Transactions, vol. 57, pp. 340-351, 2015.
  • S. Rubino, R. Bojoi, S. A. Odhano and P. Zanchetta, ”Model predictive direct flux vector control of multi three-phase induction motor drives,” IEEE Transactions on Industry Applications, vol. 54, N 5, pp. 4394 – 4404, 2018.
  • N. Djeghali, M. Ghanes, S. Djennoune and J. P. Barbot, “Sensorless fault tolerant control for induction motors, ” International Journal of Control, Automation and Systems, vol. 11, N 3, pp. 563-576, 2013.
  • N. Bounar, A. Boulkroune, F. Boudjema, M. M’Saad and M. Farza, “Adaptive fuzzy vector control for a doubly-fed induction motor,” Neurocomputing, vol. 151(Pt 2), pp. 756-769, 2015.
  • I. González-Prieto, M. J. Duran and F. J. Barrero, “Fault-tolerant control of six-phase induction motor drives with variable current injection,” IEEE Transactions on Power Electronics, vol. 32, N 10, pp. 7894-7903, 2017.
  • E. A. Mahmoud, A. S. Abdel-Khalik and H. F. Soliman, “An improved fault tolerant for a five-phase induction machine under open gate transistor faults,” Alexandria Engineering Journal, vol. 55, N 3, pp. 2609-2620, 2016.
  • S. Bednarz, “Rotor Fault Compensation and Detection in a Sensorless Induction Motor Drive,” Power Electronics and Drives, vol. 2, N 1, pp. 71-80, 2017.
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