Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 66-71 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9798350327656 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 8th International Conference on Robotics and Automation Engineering, ICRAE 2023 - Singapore, Singapur Dauer: 17 Nov. 2023 → 19 Nov. 2023 |
Abstract
Networked control systems are closed-loop feedback control systems containing system components that may be distributed geographically in different locations and interconnected via a communication network such as the Internet. The quality of network communication is a crucial factor that significantly affects the performance of remote control. This is due to the fact that network uncertainties can occur in the transmission of packets in the forward and backward channels of the system. The two most significant among these uncertainties are network time delay and packet loss. To overcome these challenges, the networked predictive control system has been proposed to provide improved performance and robustness using predictive controllers and compensation strategies. In particular, the model predictive control method is well-suited as an advanced approach compared to conventional methods. In this paper, a networked model predictive control system consisting of a model predictive control method and compensation strategies is implemented to control and stabilize a robot arm as a physical system. In particular, this work aims to analyze the performance of the system under the influence of network time delay and packet loss. Using appropriate performance and robustness metrics, an in-depth investigation of the impacts of these network uncertainties is performed. Furthermore, the forward and backward channels of the network are examined in detail in this study.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Modellierung und Simulation
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2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. S. 66-71.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Implementation and Evaluation of Networked Model Predictive Control System on Universal Robot
AU - Noroozi, Mahsa
AU - Wang, Kai
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Networked control systems are closed-loop feedback control systems containing system components that may be distributed geographically in different locations and interconnected via a communication network such as the Internet. The quality of network communication is a crucial factor that significantly affects the performance of remote control. This is due to the fact that network uncertainties can occur in the transmission of packets in the forward and backward channels of the system. The two most significant among these uncertainties are network time delay and packet loss. To overcome these challenges, the networked predictive control system has been proposed to provide improved performance and robustness using predictive controllers and compensation strategies. In particular, the model predictive control method is well-suited as an advanced approach compared to conventional methods. In this paper, a networked model predictive control system consisting of a model predictive control method and compensation strategies is implemented to control and stabilize a robot arm as a physical system. In particular, this work aims to analyze the performance of the system under the influence of network time delay and packet loss. Using appropriate performance and robustness metrics, an in-depth investigation of the impacts of these network uncertainties is performed. Furthermore, the forward and backward channels of the network are examined in detail in this study.
AB - Networked control systems are closed-loop feedback control systems containing system components that may be distributed geographically in different locations and interconnected via a communication network such as the Internet. The quality of network communication is a crucial factor that significantly affects the performance of remote control. This is due to the fact that network uncertainties can occur in the transmission of packets in the forward and backward channels of the system. The two most significant among these uncertainties are network time delay and packet loss. To overcome these challenges, the networked predictive control system has been proposed to provide improved performance and robustness using predictive controllers and compensation strategies. In particular, the model predictive control method is well-suited as an advanced approach compared to conventional methods. In this paper, a networked model predictive control system consisting of a model predictive control method and compensation strategies is implemented to control and stabilize a robot arm as a physical system. In particular, this work aims to analyze the performance of the system under the influence of network time delay and packet loss. Using appropriate performance and robustness metrics, an in-depth investigation of the impacts of these network uncertainties is performed. Furthermore, the forward and backward channels of the network are examined in detail in this study.
KW - Networked model predictive control system
KW - packet loss
KW - time delay
KW - universal robot
UR - http://www.scopus.com/inward/record.url?scp=85188513096&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2307.09076
DO - 10.48550/arXiv.2307.09076
M3 - Conference contribution
AN - SCOPUS:85188513096
SP - 66
EP - 71
BT - 2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Robotics and Automation Engineering, ICRAE 2023
Y2 - 17 November 2023 through 19 November 2023
ER -