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Revision and Comparative Study with Experimental Validation of Sliding Mode Control Approaches Using Artificial Neural Networks for Positioning Piezoelectric Actuator

Author

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  • Cristian Napole

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Oscar Barambones

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Jokin Uralde

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Isidro Calvo

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Eneko Artetxe

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Asier del Rio

    (Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

Abstract

Piezoelectric actuators are commonly used in high precision, micro-displacement applications. However, nonlinear phenomena, like hysteresis, may reduce their performance. This article compares several control approaches—based on the combination of sliding mode control and artificial neural networks—for coping with these nonlinearities and improving actuator positioning accuracy and robustness. In particular, it discusses the application of diverse order sliding mode control techniques, such as conventional, twisting algorithms, super-twisting algorithms, and the prescribed convergence law, in combination with artificial neural networks. Moreover, it validates experimentally, with a commercial piezoelectric actuator, the application of these control structures using a dSPACE 1104 controller board. Finally, it evaluates the computational time consumption for the control strategies presented. This work intends to guide the designers of PEA commercial applications to select the best control algorithm and identify the hardware requirements.

Suggested Citation

  • Cristian Napole & Oscar Barambones & Jokin Uralde & Isidro Calvo & Eneko Artetxe & Asier del Rio, 2025. "Revision and Comparative Study with Experimental Validation of Sliding Mode Control Approaches Using Artificial Neural Networks for Positioning Piezoelectric Actuator," Mathematics, MDPI, vol. 13(12), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:1952-:d:1677793
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    References listed on IDEAS

    as
    1. Zhifu Li & Jiawei Li & Tao Weng & Ziyang Zheng, 2024. "Adaptive Backstepping Time Delay Control for Precision Positioning Stage with Unknown Hysteresis," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    2. Ander Chouza & Oscar Barambones & Isidro Calvo & Javier Velasco, 2019. "Sliding Mode-Based Robust Control for Piezoelectric Actuators with Inverse Dynamics Estimation," Energies, MDPI, vol. 12(5), pages 1-19, March.
    3. Napole, Cristian & Derbeli, Mohamed & Barambones, Oscar, 2021. "A global integral terminal sliding mode control based on a novel reaching law for a proton exchange membrane fuel cell system," Applied Energy, Elsevier, vol. 301(C).
    4. Yuri B. Shtessel & Malek Ghanes & Roshini S. Ashok, 2020. "Hydrogen Fuel Cell and Ultracapacitor Based Electric Power System Sliding Mode Control: Electric Vehicle Application," Energies, MDPI, vol. 13(11), pages 1-20, June.
    5. Silvia Licciardi & Guido Ala & Elisa Francomano & Fabio Viola & Michele Lo Giudice & Alessandro Salvini & Fausto Sargeni & Vittorio Bertolini & Andrea Di Schino & Antonio Faba, 2024. "Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons," Mathematics, MDPI, vol. 12(21), pages 1-23, October.
    6. Drgoňa, Ján & Picard, Damien & Kvasnica, Michal & Helsen, Lieve, 2018. "Approximate model predictive building control via machine learning," Applied Energy, Elsevier, vol. 218(C), pages 199-216.
    7. Xiaoyuan Wang & Yaopeng Zhang & Peng Gao, 2020. "Design and Analysis of Second-Order Sliding Mode Controller for Active Magnetic Bearing," Energies, MDPI, vol. 13(22), pages 1-14, November.
    8. Cristian Napole & Oscar Barambones & Isidro Calvo & Mohamed Derbeli & Mohammed Yousri Silaa & Javier Velasco, 2020. "Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
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