Control of Drum Shear Electric Drive Using Self-Learning Artificial Neural Networks

Alibek Batyrbek (), Valeriy Kuznetsov (), Vitalii Kuznetsov (), Artur Rojek, Viktor Kovalenko, Oleksandr Tkalenko, Valerii Tytiuk and Pavlo Krasovskyi
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Alibek Batyrbek: Department of Artificial Intelligence Technologies, Faculty of Energy, Transport and Management Systems, NPJSC «Karaganda Industrial University», Republic Avenue, 30, 101400 Temirtau, KR, Kazakhstan
Valeriy Kuznetsov: Electric Energy Department, Railway Research Institute, 50 Józefa Chłopickiego Street, 04-275 Warsaw, Poland
Vitalii Kuznetsov: 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
Artur Rojek: Electric Energy Department, Railway Research Institute, 50 Józefa Chłopickiego Street, 04-275 Warsaw, Poland
Viktor Kovalenko: Department of Electrical Engineering and Cyber-Physical Systems, Y.M. Potebnia Engineering Educational and Scientific Institute, Zaporizhzhia National University, 66 Universytetska Street, 69600 Zaporizhzhia, ZR, Ukraine
Oleksandr Tkalenko: 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
Valerii Tytiuk: Department of Electromechanics, Electrotechnical Faculty, Kryvyi Rih National University, Vitaly Matusevich, Str, 11, 50027 Kryvyi Rih, DR, Ukraine
Pavlo Krasovskyi: Department of Energy, Faculty of Computer Science and Engineering, Educational and Scientific Institute “Ukrainian State University of Chemical Technology”, Ukrainian State University of Science and Technology, 8 Nauky Avenue, 49005 Dnipro, DR, Ukraine

Energies, 2025, vol. 18, issue 21, 1-26

Abstract: The objective of this work was to study the possibility of upgrading the control system of the drum shear mechanism by using neural network PI controllers to improve the efficiency of the sheet-metal cutting process. The developed detailed model of the mechanism, including a dual DC electric drive with three subordinate control loops for the voltage of the thyristor converter, current and speed of the motors, a 6-mass kinematic system with viscoelastic connections as well as a model of the metal cutting process, made it possible to uncover that the interaction of electric drives with the mechanical part leads to significant speed fluctuations during the cutting process, which worsens the quality of the sheet-metal edge. A modified system of current and speed controllers with built-in three-layer fitting neural networks as nonlinear components of proportional-integral channels is proposed. An algorithm for the fast learning of neural controllers using the gradient descent method in each cycle of calculating the controller signal is also proposed. The developed neuro-regulators make it possible to reduce the amplitude of speed fluctuations during the cutting process by four times, ensuring the effective damping of oscillations and reducing the duration of transient processes to 0.1 s.

Keywords: neural network PI controller; DC electric drive with three subordinate control loops; drum shear mechanism (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
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