Digital Twin of Industrial Networked Control System based on Value of Information

The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system's state,...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Van-Phuc, Bui, Abode, Daniel, Pedro M de Sant Ana, Muthineni, Karthik, Pandey, Shashi Raj, Popovski, Petar
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Confidence intervals
Control systems
Data acquisition
Digital twins
Extended Kalman filter
Graph neural networks
Industrial robots
Model accuracy
Network control
Polynomials
Reagents
State estimation
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Summary:The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system's state, subsequently formulating the optimal scheduling strategy for execution in the physical world. However, acquiring data crucial for efficient state estimation and control computation poses a significant challenge, primarily due to constraints such as limited network resources, partial observation, and the necessity to maintain a certain confidence level for DT estimation. We propose an algorithm based on Value of Information (VoI), seamlessly integrated with the Extended Kalman Filter to deliver a polynomial-time solution, selecting the most informative subset of sensing agents for data. Additionally, we put forth an alternative solution leveraging a Graph Neural Network to precisely ascertain the AGV's position with a remarkable accuracy of up to 5 cm. Our experimental validation in an industrial robotic laboratory environment yields promising results, underscoring the potential of high-accuracy DT models in practice.
ISSN:2331-8422