Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00601671" target="_blank" >RIV/67985556:_____/24:00601671 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0013011700003822" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0013011700003822</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0013011700003822" target="_blank" >10.5220/0013011700003822</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Identification of Piezoelectric Actuator Using Bayesian Approach and Neural Networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GC23-04676J" target="_blank" >GC23-04676J: Řiditelná úchopová mechanika: Modelování, řízení a experimenty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024)

ISBN

978-989-758-717-7

ISSN

2184-2809

e-ISSN

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Počet stran výsledku

9

Strana od-do

591-599

Název nakladatele

SCITEPRESS

Místo vydání

Setubal

Místo konání akce

Porto

Datum konání akce

18. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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