Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks

Lenka Kuklišová Pavelková, Květoslav Belda

2024

Abstract

The paper deals with a modelling and identification of a class of piezoelectric actuators intended for mechatronic and bio-inspired robotic applications. Specifically, a commercial piezoelectric bender PL140 from Physik Instrumente Co. is used. Considering catalogue/datasheet parameters, a physical model of PL140 is derived using Euler-Bernoulli beam theory. This model serves as a substitution of reality to generate proper data without potentially damaging the real actuator. However, due to its complex structure, this model cannot be used for control design. For this purpose, a Hammerstein model is proposed. It consists of a static nonlinear part describing the hysteresis and a dynamic linear part that is represented by the auto-regressive model with exogenous input (ARX model). The nonlinear part of the Hammerstein model is identified by a neural network. The Bayesian approach is used for the estimation of the ARX model parameters.

Paper Citation

in Harvard Style

Kuklišová Pavelková L. and Belda K. (2024). Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 591-599. DOI: 10.5220/0013011700003822

in Bibtex Style

@conference{icinco24,
author={Lenka Kuklišová Pavelková and Květoslav Belda},
title={Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={591-599},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013011700003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks
SN - 978-989-758-717-7
AU - Kuklišová Pavelková L.
AU - Belda K.
PY - 2024
SP - 591
EP - 599
DO - 10.5220/0013011700003822
PB - SciTePress