Torsional Vibration Adaptive Neural Network Fault-Tolerant Control of the Main Drive System for the Rolling Mill

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255750" target="_blank" >RIV/61989100:27240/24:10255750 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/24:10255750

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10666763" target="_blank" >https://ieeexplore.ieee.org/document/10666763</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2024.3454642" target="_blank" >10.1109/ACCESS.2024.3454642</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Torsional Vibration Adaptive Neural Network Fault-Tolerant Control of the Main Drive System for the Rolling Mill

Popis výsledku v původním jazyce

The main drive system of the rolling mill often experiences torsional vibrations, which severely affect product quality, precision, and the service life of the transmission equipment. This paper investigates the torsional vibration suppression problem in the main drive system of the rolling mill, considering actuator faults, nonlinear friction, nonlinear damping, and model uncertainties. Based on the high-order fully actuated (HOFA) system approach, the main drive system of the rolling mill is transformed into a rolling mill main drive fully actuated system (RMMDFAS). Adaptive neural networks are introduced to address unknown uncertainties, and a continuous differentiable Gaussian error function is used to handle actuator faults. An adaptive neural network fault-tolerant control law for motor torque is proposed. The stability of the designed main drive torsional vibration system is rigorously proven, while maintaining the performance of the transformed states. Finally, the effectiveness and superiority of the proposed algorithm are verified through simulations.

Název v anglickém jazyce

Torsional Vibration Adaptive Neural Network Fault-Tolerant Control of the Main Drive System for the Rolling Mill

Popis výsledku anglicky

The main drive system of the rolling mill often experiences torsional vibrations, which severely affect product quality, precision, and the service life of the transmission equipment. This paper investigates the torsional vibration suppression problem in the main drive system of the rolling mill, considering actuator faults, nonlinear friction, nonlinear damping, and model uncertainties. Based on the high-order fully actuated (HOFA) system approach, the main drive system of the rolling mill is transformed into a rolling mill main drive fully actuated system (RMMDFAS). Adaptive neural networks are introduced to address unknown uncertainties, and a continuous differentiable Gaussian error function is used to handle actuator faults. An adaptive neural network fault-tolerant control law for motor torque is proposed. The stability of the designed main drive torsional vibration system is rigorously proven, while maintaining the performance of the transformed states. Finally, the effectiveness and superiority of the proposed algorithm are verified through simulations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

Volume: 12, 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

125585-125591

Kód UT WoS článku

001316077600001

EID výsledku v databázi Scopus

2-s2.0-85203529768