Abstract
In this paper, the controller’s tuning and performance of a speed controller prototype for switched reluctance motor is presented using significant experimental tests. The system uses an on-line learning mechanism to acquire and modify, if needed, the “good” fuzzy control rules. Experimental essays are analyzed and discussed in order to reveal some advantages of having a learning speed controller for the SR machine, and also the drawbacks that the use of using these controllers can introduce to the drive system and possible ways to overcome them.
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References
Arabshahi P, RJ Marks II and Caudell TP (1993) Adaptation of Fuzzy Inferencing: a survey. Proceedings of the IEEE/Nagoya University WWW on Learning and Adaptive Systems, Nagoya University, pp 1–9.
Aström, KJ and Wittenmark, Computer controlled systems theory and design. Prentice-Hall, Englewood Cliffs.
Baltazar P, Silviano R, Pires AJ and Costa Branco PJ (2003) Obtaining the Magnetic Characteristics of an 8/6-Switched Reluctance Machine: FEM Analysis and Experimental Tests. IEEE International Symposium on Industrial Electronics – ISIE2003,Rio de Janeiro.
Henriques L, Costa Branco PJ, Rolim L, Suemitsu W and Dente J (2000) Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-Fuzzy Compensation. IEEE Transactions on Magnetics, Vol 36, N 5/part 1, pp 3592–3594.
Henriques L, Costa Branco PJ, Rolim L, Suemitsu W (2001) Two Automatic Online New Schemes to Compensate the Torque Ripple of Switched Reluctance Machines: With and Without Torque Signal Measurement. Soft Computing And Industry – Recent Applications, Springer-Verlag, Berlim, pp. 225–236.
Henriques L, Costa Branco PJ, Rolim L, Suemitsu W (2002) Proposition of an Off-Learning Current Modulation for Torque Ripple Reduction in Switched Reluctance Motors: Design and Experimental Evaluation. IEEE Transactions on Industrial Electronics, Vol 49, N 3, pp 665–676.
Henriques L, Rolim L, Suemitsu W, Costa Branco PJ (2004) Development and Implementation of a Neuro Fuzzy Technique for position sensor elimination in a SRM. International Symposium on Industrial Electronics, Ajaccio, France.
Miller TJE (1993) Switched Reluctance Motors and Their Control. Magna Phisics Publ. and Clarendon Press, Oxford.
Rodrigues M, Costa Branco PJ, Suemitsu W and Dente J (2001) Fuzzy-Logic Torque Ripple Reduction by Turn-off Angle Compensation for a Switched Reluctance Motor. IEEE Transactions On Industrial Electronics, Vol 48, N 3, pp 711–715.
Ertugrul N and Check AD (2000) Indirect angle Estimation in Switched Reluctance Motor Drives using Fuzzy Logic Based Motor Model. IEEE Transactions on Power Electronics Vol 15, N 6, pp 1029–1044.
Silviano R, Pires AJ and Costa Branco PJ (2003) Implementation of an 8/6 Switched Reluctance Mosfet Current Controller: Simulation Study And Experimental Tests. IEEE International Symposium on Industrial Electronics (ISIE′2003), Rio de Janeiro, Brasil. ISBN 0-7803-7912-8
Silviano R, Pires AJ and Costa Branco PJ (2003) Implementation of a Neuro-Fuzzy Speed Controller for a Switched Reluctance Machine. 8° CLEEE-Congresso Luso-Espanhol de Engenharia Electrotécnica, Vol 3, pp 6.345–6.350.
Soares F and Costa Branco PJ (2001) Simulation of a 6/4 Switched Reluctance Motor Based on Matlab/Simulink Environment. IEEE Transactions on Aerospace and Electronic Systems, Vol 37, N 3, pp 989–1099.
Wang LX and Mendel JM (1992) Generating Fuzzy Rules by Learning from Examples. IEEE Transaction on Systems Man Cybernetics, Vol 22, N 6, pp 116–132, 1414–1427.
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Rafael, S., Pires, A., Branco, P.C. (2006). Performance of a Four Phase Switched Reluctance Motor Speed Control Based On an Adaptive Fuzzy System: Experimental Tests,Analysis and Conclusions. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_45
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DOI: https://doi.org/10.1007/3-540-31662-0_45
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