Title
Development of a Digital Twin of a DC Motor Using NARX Artificial Neural Networks
Authors
[ 1 ] Department of Electrical Engineering and Electronics, National University “Odessa Maritime Academy”, Didrikhson Str., 8, 65052 Odesa, OR, Ukraine | [ 2 ] Electric Energy Department, Railway Research Institute, 50 Józefa Chłopickiego Street, 04-275 Warsaw, Poland | [ 3 ] Wydział Bezpieczeństwa Narodowego, Akademia Sztuki Wojennej | [ 4 ] John Paul II Academy in Biała Podlaska, Rector’s Office, Sidorska Str. 95/97, 21-500 Biała Podlaska, Poland | [ 5 ] Department of History of Kazakhstan and General Educational Disciplines, Faculty of Economics and Construction, Non-Profit Joint-Stock Company “Karaganda Industrial University”, Republic Avenue, 30, 101400 Temirtau, KR, Kazakhstan | [ 6 ] Department of Electrical Engineering, Faculty of Electomechanic and Electrometallurgy, Dnipro Metallurgical Institute, Ukrainian State University of Science and Technologies, 2 Lazaryana Street, 49000 Dnipro, DR, Ukraine | [ 7 ] Department of Cyberphysical and Information-Measuring Systems, Faculty of Electrical Engineering, Institute of Power Engineering, Dnipro University of Technology, 19 Dmytro Yavornytskyi Avenue, 49005 Dnipro, DR, Ukraine | [ 8 ] Department of Artificial Intelligence Technologies, Faculty of Energy, Transport and Management Systems, Non-Profit Joint-Stock Company “Karaganda Industrial University”, Republic Avenue, 30, 101400 Temirtau, KR, Kazakhstan | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Article type
scientific article
Publication language
english
Keywords
Abstract
EN This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor.
Date of online publication
11.12.2025
Pages (from - to)
1 - 24
Comments
Correspondence: vkuznetsov@ikolej.pl (V.K.); n.druzhinina@tttu.edu.kz (N.D.); v.v.kuznetsov@ust.edu.ua (V.K.). (This article belongs to the Section F5: Artificial Intelligence and Smart Energy). Nr artykułu: 6502
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Release date
11.12.2025
Date of Open Access to the publication
at the time of publication
Full text of article
Access level to full text
public
Ministry points / journal
140
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