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Improving Quality of Movement in a Linear Switched Reluctance Motor Using a Fuzzy Logic System | ||
International Journal of Industrial Electronics Control and Optimization | ||
مقاله 7، دوره 3، شماره 4، آذر 2020، صفحه 459-468 اصل مقاله (1.05 MB) | ||
نوع مقاله: Research Articles | ||
شناسه دیجیتال (DOI): 10.22111/ieco.2020.32897.1238 | ||
نویسندگان | ||
Allahverdi Azadrou1؛ Siamak Masoudi ![]() ![]() ![]() | ||
1Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. | ||
2Islamic Azad University, Abhar Branch, Abhar, IRAN | ||
3Electrical Eng. Department, Islamic Azad University, Urmia branch | ||
چکیده | ||
This work deals with minimizing fluctuations of propulsion force and improving the motion quality in a linear switched reluctance motor. In order to minimize the jerks in the moving part of the motor, a new profile has been used to generate an appropriate reference speed profile. The results indicate that at speed 0.5 m/s, the motor reaches its command speed at the proposed time while, using conventional speed profile it takes almost 1.4 times the desired time. In order to control the speed and incease the motion quality, a simple fuzzy logic system has been used which is able to overcome the uncertainties problem in nonlinear systems. The fuzzy control system can regulate the motor performance so that it tracks the reference speed with minimum error and fluctuation. To illustrate the performance of the fuzzy method, a conventional PI method along with a model reference adaptive control (MRAC) strategy have been applied to the motor and the obtained results for three control methods have been compared. Speed overshoot using conventional PI method is about 20 percent of the final speed while this is about 6 percent for fuzzy and MRAC methods. The system is designed and its efficiency is shown through simulation and experimental tests in different performance situations . The obtained results confirm that the fuzzy strategy outperforms other methods. | ||
کلیدواژهها | ||
Linear motor؛ Switched reluctance motor؛ speed control | ||
مراجع | ||
[1] Lobo, N.S., Lim, H.S., Krishnan, R.: 'Comparison of linear switched reluctance machines for vertical propulsion application: analysis, design and experimental correlation', IEEE Trans. Ind. Appl., 2008, 44, (4), pp 1134-1142. [2] Ye, J., Bilgin, B., Emadi, A., ‘An Offline Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Motor Drives’, IEEE Trans. On Energy Convers., 2015, 30, (2), pp. 726–735. [3] Shin, H.-U., Park, K., Lee, K.-B., ‘A Non-Unity Torque Sharing Function for Torque Ripple Minimization of Switched Reluctance Generators in Wind Power Systems’, Energies, 2015, 8, (10), pp. 11685–11701. [4] Li, H., Bilgin, B., Emadi, A., ‘An Improved Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Machines’, IEEE Trans. On Power Electron., 2019, 34, (2), pp. 1635-1644. [5] Moradi Cheshmehbeigi, H., and Mohamadi Amidi, A., ‘Torque ripple minimization in SRM based on advanced torque sharing function modified by genetic algorithm combined with fuzzy PSO’, International Journal of Industrial Electronics, Control and Optimization, 2018, 1, (1), pp. 71-80. [6] Masoudi. S., Feyzi, M. R., Sharifian, M. B. B.: 'Force ripple and jerk minimization in double sided linear switched reluctance motor used in elevator application', IET, Electric Power Appl., 2016, 10, (6), pp. 508-516. [7] Luis, O., Costa, B.: ‘Proposition of an off-line learning current modulation for torque-ripple reduction in switched reluctance motors: design and experimental evaluation’, IEEE Trans. On Ind. Electron., 2002, 49, (3), pp. 665-676. [8] Sanches, E.S., Santisteban, J.A., ‘Mutual Inductances Effect on the Torque of an Axial Magnetic Flux Switched Reluctance Motor’, IEEE Trans. On Lat. Am., 2015, 13, pp. 2239–2244. [9] Mikail, R., Husain, I., Islam, M.S., Sozer, Y. Sebastian, T., ‘Four-Quadrant Torque Ripple Minimization of Switched Reluctance Machine Through Current Profiling with Mitigation of Rotor Eccentricity Problem and Sensor Errors’, IEEE Trans. On Ind. Appl. 2015, 51, pp. 2097–2104. [10] Shaked, N.T., Rabinovici, R., ‘New procedures for minimizing the torque ripple in switched reluctance motors by optimizing the phase-current profile’, IEEE Trans. On Magn., 2005, 41, pp. 1184–1192. [11] Labiod, C., Srairi, K., Mahdad, B., Benbouzid, M.E.H., ‘A novel control technique for torque ripple minimization in switched reluctance motor through destructive interference’, Electr. Eng. 2017, 100, pp. 1– 10. [12] Soltanpour, M, Abdollahi, H., and Masoudi, S., ‘Optimization of double sided linear switched reluctance motor for mass and force ripple minimization’, IET Science Measurement and Technology, 2019, 13, (4), pp. 509-517. [13] Jin, W.L., Hong S.K., Byung I.K., Byung T.K., ‘New rotor shape design for minimum torque ripple of SRM using FEM’, IEEE Trans. On Magn. 2004, 40, (2), pp. 754–757. [14] Kermanipour, M.J., Ganji, B., ‘Modification in Geometric Structure of Double-Sided Axial Flux Switched Reluctance Motor for Mitigating Torque Ripple’, Canadian Journal of Elect. Comput. Eng., 2015, 38, (4), pp. 318–322. [15] Keramat, R., Ershadi, M. H., and Shojaeian, S., ‘A comparison of fuzzy and brain emotional learning-based intelligent control approaches for a full bridge DC-DC converter’, International Journal of Industrial Electronics, Control and Optimization, 2019, 2, (3), pp. 197-206. [16] Liu, Y., Wu, F., and Ban, X., ‘Dynamic output feedback control for continuous-time T-S fuzzy systems using fuzzy lyapunov functions’, IEEE Trans. On Fuzzy Systems, 2017, 25, (5), pp. 1155-1167. [17] Sun, K., Mou, S., Qiu, J., Wang, T., Gao, H., ‘Adaptive fuzzy control for nontriangular structural stochastic switched nonlinear systems with full state constraints’, IEEE Trans. On Fuzzy Systems, 2018, 27, (8), pp. 1587- 1601. [18] Masoudi, S., Soltanpour, M., Abdollahi, H., ‘Adaptive fuzzy control method for a linear switched reluctance motor’, IET Electric Power Appl., 2018, 12, (9), pp. 1328-1336. [19] Liu, Z., Lai, G.Y., and Chen, C.L.P., ‘Adaptive neural output feedback control of output constrained nonlinear systems with unknown output nonlinearity’, IEEE Trans., on Neural Netw. Learn Syst., 2015, 26, (8), pp. 1789- 1802. [20] Zhang, L., and Jia, M., ‘Robust controller design for switched fuzzy system with uncertain input’, 4th International Conf. On Information, Cybernetics and Computational Social Systems, 2017, Dalian, Chaina. [21] Xu, J., Du, Y., Chan, Y.H., Guo, H., ‘Optimal robust control design for constrained uncertain systems: a fuzzy set theoretic approach’, IEEE Trans. On Fuzzy Systems, 2018, 26, (6), pp. 3494-3505. [22] Lambrechts, P., Boerlage, M., Steinbuch, M., ‘Trajectory planning and feedforward design for high performance motion systems’, Proceedings of the 2004 American Control Conference, 2005, Boston, USA. [23] Rahideh, M., Ketabi, A., and Halvaei Niasar, A., ‘Maximum power point tracking using a state dependent riccati equation-based model reference adaptive control’, International Journal of Industrial Electronics, Control and Optimization, 2020, 3, (2), pp. 115-124. | ||
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