Modelling the neural speed regulation system of PMSM motor in Matlab-Simulink

• Autorzy: Siwek P.
• Konferencja: Computer Applications in Electrical Engineering (18-19.04.2016 ; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

To implement speed regulation algorithms of PMSM motor on real object the exact model encompassing imperfections of digital measurement and AC-DC-AC converter is required. The aim of the following paper is to present this kind of simulation model of the neural speed regulation system of PMSM motor in Matlab – Simulink. In the beginning the direct-quadrature transform and mathematical model of permanent magnet synchronous motor in dq coordinate system are shown. The paper shows speed measurement algorithm which ensure digital character of the output data. In the second section the neural speed controller trained on-line by backpropagation algorithm and the method for calculating gradient of error function are described. At the end of the paper simulation results validating proper behaviour of neural speed regulation system of PMSM motor are shown.

Słowa kluczowe

EN

artificial neural networks; speed control; PMSM; motor drive model; direct-quadrature; speed measurement; back-propagation algorithm

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Poznan University of Technology Academic Journals. Electrical Engineering
• Rocznik: 2016
• Tom: No. 85
• Strony: 365--376
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Siwek P.

• Poznan University of Technology

Bibliografia

• [1] Bonert R., Digital Tachometer with Fast Dynamic Response Implemented by a Microprocessor, IEEE Trans. Ind. Applic., 1983.
• [2] Bose B.K., Neural network applications in power electronics and motor drives – An introduction and perspective, IEEE Trans. Ind. Electron., Feb. 2007.
• [3] Brock S., Zawirski K., Speed Measurement Method for Digital Control System, 9th EPE, 2001.
• [4] Duesterhoeft W.C., Schulz M.W., Clarke E., Determination of Instantaneous Currents and Voltages by Means of Alpha, Beta and Zero Components, Transactions of the American Institute of Electrical Engineers, 1951.
• [5] Ertan H.B., Uctug M.Y., Colyer R., Consoli A., Modern Electrical Drives, Springer, 2000.
• [6] Kovacs K.P., Racz J., Transiente Vorgange in Wechselstrommachinen, Budapest, Hungary, Akad. Kiado, 1959.
• [7] Noriega J.R., Wang H., A direct adaptive neural network control for unknown nonlinear systems and its application, American Control Conference, 1995.
• [8] Pajchrowski T., Zawirski K., Nowopolski K., Neural Speed Controller Trained On-Line by Means of Modified RPROP Algorithm, IEEE Trans. Ind. Inf., 2015.
• [9] Park R.H., Two Reaction Theory of Synchronous Machines, AIEE, N. Y., 1929.
• [10] Rutkowski L., Computational Intelligence Methodts and Techniques, Springer-Verlag, 2008.
• [11] Zawirski K., Deskur J., Kaczmarek T., Automatyka Napędu Elektrycznego, Wydawnictwo Politechniki Poznańskiej, 2012, (in Polish).

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.