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High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Author

Listed:
  • Cristian Napole

    (System Engineering and Automation Deparment, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Oscar Barambones

    (System Engineering and Automation Deparment, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Mohamed Derbeli

    (System Engineering and Automation Deparment, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Isidro Calvo

    (System Engineering and Automation Deparment, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Mohammed Yousri Silaa

    (System Engineering and Automation Deparment, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Javier Velasco

    (Fundación Centro de Tecnologías Aeronáuticas (CTA), Juan de la Cierva 1, 01510 Miñano, Spain)

Abstract

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

Suggested Citation

  • Cristian Napole & Oscar Barambones & Mohamed Derbeli & Isidro Calvo & Mohammed Yousri Silaa & Javier Velasco, 2021. "High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks," Mathematics, MDPI, vol. 9(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:244-:d:487189
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    References listed on IDEAS

    as
    1. Mohammed Yousri Silaa & Mohamed Derbeli & Oscar Barambones & Ali Cheknane, 2020. "Design and Implementation of High Order Sliding Mode Control for PEMFC Power System," Energies, MDPI, vol. 13(17), pages 1-15, August.
    2. Cristian Napole & Oscar Barambones & Isidro Calvo & Javier Velasco, 2020. "Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules," Energies, MDPI, vol. 13(15), pages 1-16, August.
    3. Fardila Mohd Zaihidee & Saad Mekhilef & Marizan Mubin, 2019. "Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review," Energies, MDPI, vol. 12(9), pages 1-27, May.
    4. Hyung Keun Ahn & Neungsoo Park, 2021. "Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors," Energies, MDPI, vol. 14(2), pages 1-17, January.
    5. Hao Lin & Jose I. Leon & Wensheng Luo & Abraham Marquez & Jianxing Liu & Sergio Vazquez & L. G. Franquelo, 2020. "Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters," Energies, MDPI, vol. 13(1), pages 1-20, January.
    6. 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.
    7. Alexander Alyukov & Yuri Rozhdestvenskiy & Sergei Aliukov, 2020. "Active Shock Absorber Control Based on Time-Delay Neural Network," Energies, MDPI, vol. 13(5), pages 1-16, March.
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