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Vol: 51(65) No: 4 / December 2006 Neuro-Fuzzy Control and Classic PI Control - Comparative Analysis for Electrical Drives Equipped with Permanent Magnet Synchronous Machine Octavian Prostean Department of Automation and Applied Informatics, Faculty of Automation and Computer Sciences, “Politehnica” University of Timisoara, V. Parvan, No.2, 300223, Timisoara, Romania, phone: (0040) 256 403237, e-mail: octavian.prostean@aut.upt.ro, web: http://www.aut.utt.ro/~prostean Ioan Filip Department of Automation and Applied Informatics, Faculty of Automation and Computer Sciences, “Politehnica” University of Timisoara, V. Parvan, No.2, 300223, Timisoara, Romania Cristian Vasar Department of Automation and Applied Informatics, Faculty of Automation and Computer Sciences, “Politehnica” University of Timisoara, V. Parvan, No.2, 300223, Timisoara, Romania Iosif Szeidert Department of Automation and Applied Informatics, Faculty of Automation and Computer Sciences, “Politehnica” University of Timisoara, V. Parvan, No.2, 300223, Timisoara, Romania Keywords: permanent magnet synchronous machines (PMSM), electrical drives, control structures, modeling and simulation, MATLAB-Simulink, PI control, neuro- fuzzy controllers. Abstract The paper presents a comparative analysis regarding the control of electrical drives used for permanent magnet synchronous machines (PMSM). All the studies are made using the system models implemented in MATLAB – Simulink, analysis’s being focused on the two considered control structures: the first based on the usage of a classic PI controller, respectively the second using a neuro-fuzzy controller. Also the study cases considered different functioning regimes of permanent magnet synchronous machine. Finally some relevant conclusion regarding these two control structures are exposed with practical applications. The core of the model is designed based on MATLAB - PSB (Power System Blockset) toolbox. References [1] M. Cristea, A. Dinu, M. McCormick and J. G. Khor, Neural and Fuzzy Logic Control of Drives and Power Systems, Newnes; 1st edition (August 15, 2002), 399 pp. [2] L.-A. Dessaint, K. Al-Haddad, H. Le-Huy, G. Sybille and P. Brunelle, “A Power System Simulation Tool Based on Simulink”, IEEE Transactions on Industrial Electronics, vol. 46, no. 6, pp. 1252-1254, December 1999. [3] Gh. D. Andreescu, Estimatoare in Sisteme de Conducere a Actionarilor Electrice – Aplicatii la Masini Sincrone cu Magneti Permanenti, Editura Orizonturi Universitare, Timisoara, 1999. [4] St. Preitl and R.-E. Precup, Fuzzy Controllers, Editura Orizonturi Universitare, Timisoara, 1999. [5] F. Cupertino, M. Dotoli, V. Giordano, B. Maione and L. Salvatore, “Fuzzy Control Experiments on DC Drives Using Various Inference Connectives”, Proc. WCCI \'02 IEEE World Congress on Computational Intelligence, Honolulu, HI, 2002. [6] G. Zhu, Louis-A. Dessaint, O. Akhrif and A. Kaddouri, “Speed Tracking Control of a Permanent Magnet Synchronous Motor with State and Load Torque Observer”, IEEE Transactions on Industrial Electronics, vol. 47, no. 2, April 2002. [7] Z. Ibrahim and E. Levi, “Fuzzy Logic Versus PI Speed Control in High-Performance AC Drives: A Comparison,” Electric Power Components and Systems, Publisher Taylor & Francis, vol. 31, no. 4, pp. 403-422, April 2003. [8] M. Malinowski and M. P. Kazmierkowski, “Simple Direct Power Control of Three-Phase PWM Rectifier Using Space Vector Modulation – A Comparative Study”, EPE Journal, vol. 13, no. 2, pp. 28-34, 2003. [9] I. Filip, O. Prostean and D. I.Curiac, “Tuning Considerations Above a Fuzzy Controller Used for the Synchronous Generator”, Lecture Notes in Computer Science, vol. 1625, Springer Verlag, “Computational Intelligence – Theory and Applications”, Dortmund, Germany, pp. 632-641, 1999. [10] MATLAB, Simulink – Documentation: www.mathworks.com |