IDENTIFICATION OF MATHEMATICAL MODEL IN THE FORM OF POLYNOMIAL RECURRENT NEURAL NETWORK AND ADJUSTMENT OF ELECTRIC DRIVE WITH SERIES-WOUND MOTOR

Authors

  • I. A. Orlovskiy

DOI:

https://doi.org/10.15588/1607-3274-2011-1-26

Keywords:

mathematical model, DC motor of series excitation, recurrent neural network, speed controller, parameters identification.

Abstract

Mathematical models of electric drive with a series-wound motor in the form of polynomial recurrent neural networks (PRNN) have been synthesized using its operational data. The methods of identification of drive parameters and drag torque dependence on motor speed were studied, setting special operating modes and different types of equations describing parameters non-linearity. A PI speed controller was adjusted according to the obtained model in the form of PRNN.

Downloads

Published

2011-08-12

How to Cite

Orlovskiy, I. A. (2011). IDENTIFICATION OF MATHEMATICAL MODEL IN THE FORM OF POLYNOMIAL RECURRENT NEURAL NETWORK AND ADJUSTMENT OF ELECTRIC DRIVE WITH SERIES-WOUND MOTOR. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2011-1-26

Issue

No. 1 (2011): Radio Electronics, Computer Science, Control

Section

Theory and methods of automatic control

License

Copyright (c) 2014 I. A. Orlovskiy

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons Licensing Notifications in the Copyright Notices

The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.

The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.

The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.