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Object-oriented DSP implementation of neural state estimator for electrical drive with elastic coupling

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Warianty tytułu
Computer Applications in Electrical Engineering (10-11.04.2017 ; Poznań, Polska)
Języki publikacji
The study presents results and procedure of object-oriented and test-driven implementation of neural-network-based state estimator. The presented algorithm has been developed for estimation of the state variables of the mechanical part of electric drive with elastic coupling. Estimated state variables – load speed and shaft stiffness torque – can be used in speed control process for reducing mechanical vibrations of working machine. The basic objective was to create a simple, extensible and readable program code, performing the task of state estimation of the considered system. The target platform is a DSP (Digital Signal Processor) from SHARC (Super Harvard architecture Single-Chip Computer) family, which allows for hardware acceleration of matrix operations. The IDE (Integrated Development Environment) available for the selected platform made it possible to write program in C++. The usage of UML (Unified Modelling Language) in the development of control software was discussed.
Opis fizyczny
Bibliogr. 10 poz., rys., tab.
  • Poznan University of Technology
  • Poznan University of Technology
  • [1] ŁUCZAK D., WÓJCIK A. DSP implementation of state observers for electrical drive with elastic coupling. Przegląd Elektrotechniczny. 2016, Number 5, p. 100–105.
  • [2] JOBLING C.P., GRANT P.W., BARKER H.A., TOWNSEND P. Object–oriented programming in control system design: a survey. Automatica. August 1994, Volume 30, Number 8, p. 1221–1261.
  • [3] ŁUCZAK D. Mathematical model of multi–mass electric drive system with flexible connection. In: Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On. September 2014, p. 590–595.
  • [4] ORLOWSKA–KOWALSKA T., KAMINSKI M. Optimization of neural state estimators of the two–mass system using OBD method. In: IEEE International Symposium on Industrial Electronics, 2008, ISIE 2008, June 2008, p. 461–466.
  • [5] ORLOWSKA–KOWALSKA T., SZABAT K. Neural–Network Application for Mechanical Variables Estimation of a Two–Mass Drive System. IEEE Transactions on Industrial Electronics, June 2007, Volume 54, Number 3, p. 1352–1364.
  • [6] SZABAT K., ORLOWSKA–KOWALSKA T. Vibration Suppression in a Two–Mass Drive System Using PI Speed Controller and Additional Feedbacks – Comparative Study. IEEE Transactions on Industrial Electronics, April 2007, Volume 54, Number 2, p. 1193–1206.
  • [7] SZABAT K., ORLOWSKA–KOWALSKA T. Optimization of the two–mass drive dynamics using different compensation feedbacks. In: 11th International Conference on Optimization of Electrical and Electronic Equipment, 2008, OPTIM 2008, May 2008, p. 19–24.
  • [8] YADAIAH N., SOWMYA G. Neural Network Based State Estimation of Dynamical Systems. In: International Joint Conference on Neural Networks, 2006, IJCNN ’06, 2006, p. 1042–1049.
  • [9] TIOBE Index | Tiobe – The Software Quality Company [online]. [7.6.2016]. Downloaded:
  • [10] CrossCore Embedded Studio 2.0.0 C/C++ Library Manual for SHARC Processors – cces–2–0–0_SharcLibrary_mn_rev1–3.pdf [online]. [19.2.2016]. Downloaded:–documentation/software–manuals/cces–2–0–0_SharcLibrary_mn_rev1–3.pdf.
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
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