Hybrid NARX Neural Network with Model-Based Feedback for Predictive Torsional Torque Estimation in Electric Drive with Elastic Connection

Amanuel Haftu Kahsay, Piotr Derugo (), Piotr Majdański and Rafał Zawiślak
Additional contact information
Amanuel Haftu Kahsay: Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Piotr Derugo: Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Piotr Majdański: Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Rafał Zawiślak: Instytut Automatyki, Politechnika Łódzka, ul. Stefanowskiego 18, 90-537 Łódź, Poland

Energies, 2025, vol. 18, issue 14, 1-22

Abstract: This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing.

Keywords: NARX NN; electric drives; torsional vibrations damping; IP controller; predictive estimator (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/14/3770/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/14/3770/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:14:p:3770-:d:1702941

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().