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Application of iterative learning control for ripple torque compensation in PMSM drive

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time- consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.
Rocznik
Strony
309--324
Opis fizyczny
Bibliogr. 17 poz., rys., wz.
Twórcy
  • Institute of Control, Robotics and Information Engineering, Poznań University of Technology Piotrowo 3a, 60-965 Poznań, Poland
  • Institute of Control, Robotics and Information Engineering, Poznań University of Technology Piotrowo 3a, 60-965 Poznań, Poland
Bibliografia
  • [1] Brock S., Luczak D.,Nowopolski K., Pajchrowski T., Zawirski K., Two Approaches to Speed Control for Multi-Mass System with Variable Mechanical Parameters, IEEE Transactions Industrial Electronics, vol. 64, no. 4, pp. 3338–3347 (2017).
  • [2] Holtz J., Springop L., Identification and Compensation of Torque Ripple in High-Precision Permanent Magnet Motor Drives, IEEE Transaction on Industrial Electronics, vol. 43, no. 2, pp. 309–320 (1996).
  • [3] Yang J., Chen W., Li S., Guo L., Yan Y., Disturbance/Uncertainty Estimation and Attenuation Techniques in PMSM Drives – A Survey, IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 3273–3285 (2017).
  • [4] Sikorski A., Grodzki R., A new DTC control for PMSM with torque ripple minimization and constant switching frequency, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 30, no. 3, pp. 1069–1081 (2011).
  • [5] Martinez J., Krischan K., Muetze A., Minimization of a SynRel’s oscillating torque by calculation of the appropriate skew angle, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 36, no. 3, pp. 824–835 (2017).
  • [6] Sergeant P., Crevecoeur G., Dupré L., Van den Bossche A., Characterization and optimization of a permanent magnet synchronous machine, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 28, no. 2, pp. 272–285 (2009).
  • [7] Ferreti G., Magnani G., Rocco P., Modeling, Identification, and Compensation of Pulsating Torque for PMAC Machines, IEEE Transaction on Industrial Electronics, vol. 45, no. 6, pp. 912–920 (1998).
  • [8] Grcar B., Cafuta P., Stumberger G., Stanković A.M., Control-Based Reduction of Pulsating Torque for PMAC Machines, IEEE Transaction on Energy Conversion, vol. 17, no. 2, pp. 169–175 (2002).
  • [9] Pipeleers G., Moore K.L., Unified Analysis of Iterative Learning and Repetitive Controllers in Trial Domain, IEEE Transaction on Automatic Control, vol. 59, no. 4, pp. 953–965 (2014).
  • [10] Mattavelli P., Tubiana L., Zigliotto M., Torque-Ripple Reduction in PM Synchronous Motor Drives Using Repetitive Current Control, IEEE Transaction on Power Electronics, vol. 20, no. 6, pp. 1423–1431 (2005).
  • [11] Xia Ch., DengW., Shi T., Yan Y., Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control, Journal of Electrical Engineering and Technology, vol. 11, no. 2, pp. 425–436 (2016).
  • [12] Pajchrowski T., Zawirski K., Brock S., Application of neuro-fuzzy techniques to robust speed control of PMSM, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 26, no. 4, pp. 1188–1203 (2007).
  • [13] Černigoj A., Gašpari L., Fišer R., Native and Additional Cogging Torque Components of PM Synchronous Motors – Evaluation and Reduction, Automatika, 2017, vol. 51, no. 2, pp. 157–165 (2010).
  • [14] Bose B.K., Chio K.M., Kim H.J., Self Tunning Neural Network Controller for Induction Motor Drives, IEEE Annual Conference of the IEEE Industrial Electronics Society, Sevilla, Spain, pp. 152–156 (2002).
  • [15] Stamenković I., Jovanović D., Vukosavić S., Torque ripple Verification in PM Machines, EUROCON 2005 – The International Conference on “Computer as a Tool”, Serbia & Montenegro, Belgrade, pp. 1497–1500 (2005).
  • [16] Reynaldi A., Lukas S., Margaretha H., Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation, Malta, Valetta, pp. 89–94 (2012).
  • [17] Vas P., Sensorless Vector and Direct Torque Control, Oxford University Press (1998).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0f9f2c86-dc5d-4e0c-ad25-9b5dddbff30f
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