COMPARISON OF THE VARIOUS CONTROLS OF THE SWITCHED RELUCTANCE MOTOR 12/8
DOI:
https://doi.org/10.59277/RRST-EE.2023.68.1.9Keywords:
Switched reluctance machine (SRM), Proportional–Integral–Derivative (PID), Controller, Neural Command, Fuzzy Logic ControlAbstract
The control of the variable reluctance machine raises a certain number of constraints, among which we can cite: the effect of the external disturbances, the nature of the nonlinearities, the ripple of its torque, and the modeling errors. The different ordering approaches proposed in this article dealt with all these constraints. The classical-control algorithms, for example, of derived full proportional action may prove sufficient if the requirements on the accuracy and performance of systems are flexible. In the opposite case, particularly when the controlled part is submitted to strong nonlinearity and temporal variations, control techniques must be designed to ensure the process's robustness concerning the uncertainties on the parameters and their variations. These techniques include artificial intelligence-based techniques constituted of neural networks and fuzzy logic. This technique can replace PID regulators with nonlinear ones using the human brain’s reasoning and functioning and is simulated using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.
References
(1) O.K. Krinah, R. Lalalou, Z. Ahmida, S. Oudina, Performance investigation of a wind power system based on double-feed induction generator: fuzzy versus proportional integral controllers, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 4, pp. 403–408, Bucarest (2022).
(2) K.R. Chichate, S.R. Gore, A. Zadey, Simulation of Switched Reluctance Motor for Speed Control Applications, 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India (5-7 March 2020).
(3) J.W. Ahn, G.F. Lukman, Switched reluctance motor: Research trends and overview, CES Transactions on Electrical Machines and Systems, 2, 4, pp. 339 - 347 (Dec. 2018).
(4) D.F. Valencia, R. Tarvirdilu-Asl, C. Garcia, J. Rodriguez et al. A review of predictive control techniques for switched reluctance machine drives. Part I: Fundamentals and Current Control, IEEE Transactions on Energy Conversion, 36, 2, pp. 1313 - 1322 (29 Dec 2020).
(5) A. Tahour, A.G. Aissaoui, A.C. Megherbi, Position control of switched reluctance motor using an adaptive backstepping controller, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 56, 3, pp. 314-324 (2011).
(6) J. Han, G. Baojun, K. Zhang, Y. Wang, C. Wang, Influence of control and structure parameters on the starting performance of a 12/8 pole switched reluctance, Energies, A6: Electric Vehicles, 13, 14, pp. 3744, July (2020).
(7) I.A. Viorel, L. Strete, I.F. Soran, Analytical flux linkage model of switched reluctance motor, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg.,54, 2, pp. 139–146, Bucarest (2009).
(8) P. Srinivas, Implementation of PWM control of dc split converter fed switched reluctance motor drive. International Journal of Electrical and Computer Engineering IJECE. 7, 2, pp. 604-609 (April 2017).
(9) M. Chouitek, Control of a variable reluctance motor by the use of artificial intelligence. Doctoral thesis, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (2016).
(10) A. Tahour, Neural controller for a switched reluctance machine, Revue Roumaine des Sciences Techniques - Série Électrotechnique et Énergétique, 53, 4, pp. 473-482 (2008).
(11) M. Habbab, A. Hazzab, P. Sicard, real time implementation of fuzzy adaptive PI-sliding mode controller for induction, International Journal of Electrical and Computer Engineering, 8, 5, pp. 2883-2893 (2018)
(12) E. Daryabeigi, A.H. Zarchi, G.R.A. Markadeh, J. Soltani, R.A, F. Blaabjerg, Online MTPA control approach for synchronous reluctance motor drives based on emotional controller. IEEE Transactions on Power Electronics, 30, pp. 2157-2166 (2015).
(13) M. Ferrari, N. Bianchi, E. Fornasiero, Analysis of rotor saturation in synchronous reluctance and pm-assisted reluctance motors. IEEE Transactions on Industry Applications, 51, pp. 169-177 (2015)
(14) D. Flieller, N.K. Nguyen, P. Wira, G. Sturtzer, D.O. Abdeslam, J. Merckle Self-learning solution for torque ripple reduction for nonsinusoidal permanent magnet motor drives based on artificial neural networks, IEEE Transactions on Industrial Electronics, 61, pp. 655-666 (2014).
(15) D. Csaba, A. Binder, Design of compact permanenet-magnet synchronous motors with concentrated windings, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 52 2, pp. 182-198 (2007).
(16) K. Makhloufi, S. Zegnoun, A. Omari, I.K. Bousserhane, Commande neuro-floue-glissant adaptatif d'unemachine synchrone linéaire, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 4, pp. 425–431, Bucarest (2022).
(17) M. Gamal, H. Hashem, Speed control of switched reluctance motor based on fuzzy logic controller, Proc. of the 14th Int.l Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt. Paper ID 166, Pp. 19-21 (2010).
