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Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

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)

  • Isidro Calvo

    (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

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3929-:d:393121
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    References listed on IDEAS

    as
    1. Takashi Ozaki & Norikazu Ohta, 2020. "Power-Efficient Driver Circuit for Piezo Electric Actuator with Passive Charge Recovery," Energies, MDPI, vol. 13(11), pages 1-15, June.
    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. 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.
    4. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
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    Cited by:

    1. 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.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2021. "Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System," Mathematics, MDPI, vol. 9(17), pages 1-18, August.
    3. 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.

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