Inductance Estimating of Linear Switched Reluctance Motors with the Use of Adaptive Neuro-Fuzzy Inference Systems

Year 2009, Volume: 22 Issue: 2, 89 - 96, 22.03.2010

Ferhat Daldaban , Nurettin Ustkoyuncu

Abstract

 In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the phase current of the three-phase LSRM are used to estimate the phase inductance. The phase inductance results estimated by ANFIS are in very good agreement with the results of finite element analysis (FEA).

 Key Words: Linear Switched Reluctance Motor, ANFIS, Inductance.

 

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References

• Miller, T.J.E., “Switched reluctance motor drives a reference book of collected papers”, Intertec Communications Inc., California, (1988).
• Miller, T.J.E., “Switched reluctance motors and their control”, Oxford University Press, Oxford, (1993).
• Krishnan, R., “Switched reluctance motor drives modeling, simulation, analysis, design and applications”, CRC Press, London, (2001).
• Corda, J., Stephenson, J.M., “Analytical estimation of the minimum and maximum inductances of a double-salient motor”, Proc. Int. Conf. on Stepping Motors and Systems, 50-59 (1979).
• Ray, W.F., Davis, R.M., “Inverter drive for doubly-salient reluctance motor: its fundamental behavior, linear analysis and cost implications”, IEE Electric Power App. 2 (6): 185-193 (1979).
• Deshpande, U.S., Cathey, J.J. , Richter, E. “High- force density linear switched reluctance motors”, IEEE Trans. on Industry Applications, 31(2): 345-352 (1995).
• Deshpande, U., “Two-dimensional finite-element analysis of a high-force-density linear switched reluctance machine including three-dimensional effects”, IEEE Trans. on Industry Applications, 36 (4): 1047-1052 (1995).
• Bae, H.K., Lee, B.S., Vijayraghavan, P., Krishnan, R., “A linear switched reluctance motor: converter and control”, IEEE Trans. on Industry Applications, 36 (5): 1351-1359 (2000).
• Lee, B.S., Bae, H.K., Vijayraghavan, P., Krishnan, R., “Design of a linear switched reluctance machine”, IEEE Trans. on Industry Applications, 36 (6): 1571-1580 (2000).
• Gan, W.C., Cheung, N.C., Qiu, L., “Position control of linear switched reluctance motors for high-precision applications”, IEEE Trans. on Industry Applications, 39 (5): 1350-1362 (2003).
• Stumberger, G., Stumberger, B., Dolinar, D., “Identification of linear synchronous reluctance motor parameters”, IEEE Trans. on Industry Applications, 40(5): 1317-1324 (2004).
• Lindsay, J.F., Arumugam, R., Krishnan, R., “Finite-element analysis characterization of a switched reluctance motor with multi-tooth per stator pole”, Proc. IEE., 133: 347-353 (1986).
• Fulton, N.N., The application of CAD to switched reluctance drives, Int. Conf. on Electric Machines and Drives, 275-279 (1987).
• Radun, A.V., “Design considerations for the switched reluctance motor”, IEEE Trans. on Industry Applications, 31 (5): 1079-1087 (1995).
• Omekanda, A.M., Broche, C., Renglet, M., “Calculation of the electromagnetic parameters of a switched reluctance motor using an improved FEM-BIEM application to different models for the torque calculation”, IEEE Trans. on Industry Applications, 33 (4): 914-918 (1997).
• Jang, J.S.R., “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Trans on Systems Man and Cybernetics, 23 (3): 665-685 (1993).
• Jang, J.S.R., Sun, C.T., Mizutani, E., “Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence”, Prentice-Hall, Upper Saddle River, (1997).
• Brown, M., Haris, C., “Neurofuzzy adaptive modeling and control”, Prentice-Hall, Englewood Cliffs, (1994).
• Constantin, V.A., “Fuzzy logic and neuro-fuzzy applications explained”, Prentice-Hall, Englewood Cliffs, (1995).
• Lin, C.T., Lee, C.S.G., “Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems”, Prentice-Hall, Upper Saddle River, (1996).
• Kim, J., Kasabov, N., “HyFIS: Adaptive neuro- fuzzy inference systems and their application to nonlinear dynamical systems”, Neural Networks, 12 (9): 1301-1319 (1999).
• Paramasivam, S., Arumugam, R., Umamaheswari, B., Vijayan, S., Balamurugan, S., Vasudevan, M., “Indirect rotor position estimation of switched reluctance motor using ANFIS”, The Fifth Int. Conf. on Power Electronics and Drive Systems PEDS, 921-926 (2003).
• Akcayol, M.A., “Application of adaptive neuro- fuzzy controller for SRM”, Advances in Engineering Software, 35 (3-4): 129-137 (2004).
• Akcayol, M.A., Elmas, C., “NEFCLASS-based neuro-fuzzy controller for SRM drive”, Engineering Applications of Artificial Intelligence, 18: 595-602 (2005).
• Daldaban, F., Ustkoyuncu, N., Guney, K., “Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system”, Energy Conversion and Management, 47: 485-493 (2006).