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Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Author

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  • Grzegorz Kaczmarczyk

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

  • Radoslaw Stanislawski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

  • Jaroslaw Szrek

    (Department of Fundamentals of Machine Design and Mechatronic Systems, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-372 Wroclaw, Poland)

  • Marcin Kaminski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

Abstract

External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

Suggested Citation

  • Grzegorz Kaczmarczyk & Radoslaw Stanislawski & Jaroslaw Szrek & Marcin Kaminski, 2024. "Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft," Energies, MDPI, vol. 17(4), pages 1-28, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:833-:d:1336633
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    References listed on IDEAS

    as
    1. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    2. Atia Ferdousee, 2022. "Impact of Electric Vehicle Adoption on Electricity Consumption and Generation: Evidence from California," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 101-110, September.
    3. M. E. Akbari & M. A. Badamchizadeh & M. A. Poor, 2012. "Implementation of a Fuzzy TSK Controller for a Flexible Joint Robot," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-21, October.
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