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vol 67 / April, 2024
Article

DOI 10.17586/0021-3454-2019-62-6-576-584

UDC 681.5.015.8

PARAMETRIC IDENTIFICATION OF A SERVO DRIVE MODEL WITH DEADTIME-TYPE NONLINEARITIES

T. Orłowska-Kowalska
Wroclaw University of Science and Technology, Department of Electrical Machines, Drives and Measurements;


S. Y. Lovlin
ITMO University, Saint Petersburg, 197101, Russian Federation; Associate Professor


M. . Tsvetkova
ITMO University; student


A. A. Abdullin
ITMO University, Saint Petersburg, 197101, Russian Federation; Associate Professor


A. G. Mamatov
ITMO University, Department of Electrotechnics and Precision Electromechanical Systems; Post-Graduate Student


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Abstract. An approach to automatic identification of electrical parameters of a servo drive and a PWM inverter, based on least squares method, is proposed. It is noted that existing methods of parametric identification of servo drives use a linear mathematical model of the object and do not consider nonlinearity, due to the "dead time" and the voltage drop on the power switches of the converter. The output voltage of the PWM inverter is significantly distorted due to these features, which deteriorate the identification results. The proposed identification method uses a model that considers the deadtime effect and the voltage drop on the converter power switches. The new method is shown to enable a higher accuracy of parameter estimation than the method with linear servo drive model.
Keywords: identification, servo drive, dead time, PWM - inverter, least squares method

References:
  1. Sadovnikov M.A., Tomasov V.S., Tolmachev V.A. Journal of Instrument Engineering, 2011, no. 6(54), pp. 81–86. (in Russ.)
  2. Lovlin S.Yu., Polyakov N.A., Abdullin A.A., Lukichev D.V., Demidova G.L. Journal of Instrument Engineering, 2018, no. 8(61), pp. 706–712. (in Russ.)
  3. Lovlin S.Y., Tsvetkova M.H., Subbotin D.A. Advances in Automatic Control: Proceedings of the 16th International Conference on Automatic Control, Modelling & Simulation (ACMOS '14), 2014, nо. 35, рр. 199 206.
  4. Tomasov V.S., Lovlin S.Yu., Tushev S.A., Smirnov N.A. Vestnik IGEU, 2013, no. 1, pp. 84–87. (in Russ.)
  5. Krause P.C. Analysis of Electric Machinery, NY, McGraw-Hill, 1986.
  6. Si G., Shen Z., Zhang Z. and Kennel R. 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC), Auckland, 2016, pp. 1–6.
  7. Anuchin A., Gulyaeva M., Briz F. and Gulyaev I. 2017 International Conference on Modern Power Systems (MPS), Cluj-Napoca, 2017, pp. 1–6.
  8. Munoz-Garcia A. and Lipo T.A. Proc. IEEE Applicat. Power Electon. Conf., 1998, pp. 95–100.
  9. Urasaki N., Senjyu T., Uezatoand K., and Funabashi T. IEEE Trans. Energy Convers., 2007, vol. 22, pp. 271–280.
  10. Qiu T., Wen X. and Zhao F. IEEE Transactions on Power Electronics, 2016, no. 3(31), pp. 2530–2538.
  11. Alawieh H., Riachy L., Arab Tehrani K., Azzouz Y. and Dakyo B. IECON 2016 – 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016, pp. 3153–3159.
  12. Alexandrou A.D., Adamopoulos N.K. and Kladas A.G. IEEE Transactions on Industrial Electronics, 2016, no. 8(63), pp. 5167–5175.
  13. Attaianese C., Nardi V., and Tomasso G. IEEE Trans. Ind. Appl., 2005, vol. 41, pp. 1667–1674.
  14. Pillai M.S. and Vijina K. 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), Kannur, 2018, pp. 405–409.
  15. Xiao L., Tao W., and Wei F. Proc. 11th International Conference on Control, Automation and Systems (ICCAS 2011), 2011, pp. 1570–1575.
  16. Lovlin S.Yu., Mamatov A.G. Journal of Instrument Engineering, 2018, no. 10(61), pp. 897–907.
  17. Ljung L. System Identification: Theory for the User, MIT Press, Cambridge, MA, 1980.
  18. Omrane I., Etien E., Bachelier O., and Dib W. Proc. 39th Annu. IEEE IECON, 2013, pp. 2578–2583.