Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks

0601671 20250317084246.120241125235959.9 10.5220/0013011700003822 Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks 9 s. E cav_un_epca*0601670978-989-758-717-72184-2809Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024)591-599SetubalSCITEPRESS2024GiniGiuseppinaPrecupRadu-EmilFilevDimitar Piezoceramic Actuator Hammerstein Model Bayesian Estimation ARX Model Physical Modelling Euler–Bernoulli Beam Theory cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101064 Belda Květoslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/AS/kuklisova-0601671.pdf https://www.scitepress.org/Link.aspx?doi=10.5220/0013011700003822 GC23-04676J GA ČR CZ cav_un_auth*0453493 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. cav_un_auth*0477334 International Conference on Informatics in Control, Automation and Robotics 2024 (ICINCO 2024) /21./ 20241118 20241120 Porto PT BC 20000 20200 20205 2025 2 PR UTIA-B BC RVO:67985556 https://hdl.handle.net/11104/0359677 S 2024 SCOPUS PUBMED WOS cav_un_epca*0601670 Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024) SCITEPRESS 2024 Setubal 591 599 978-989-758-717-7 2184-2809 Gini Giuseppina 340 Precup Radu-Emil 340 Filev Dimitar 340