Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43972137" target="_blank" >RIV/49777513:23220/22:43972137 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2078-2489/13/7/321" target="_blank" >https://www.mdpi.com/2078-2489/13/7/321</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/info13070321" target="_blank" >10.3390/info13070321</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems

Popis výsledku v původním jazyce

Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively.

Název v anglickém jazyce

Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems

Popis výsledku anglicky

Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Information

ISSN

2078-2489

e-ISSN

2078-2489

Svazek periodika

13

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000832030900001

EID výsledku v databázi Scopus

2-s2.0-85133694208