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

TL;DR

This paper introduces a VoI-based algorithm combined with GNNs to optimize sensing in an industrial networked control system, enabling accurate digital twin modeling and state estimation for autonomous vehicles under resource constraints.

Contribution

It presents a novel VoI-based sensing selection algorithm integrated with EKF and a GNN approach for precise AGV positioning in industrial settings.

Findings

VoI-based method effectively selects informative sensors under resource constraints.

GNN achieves position accuracy up to 5 cm.

Experimental results validate high-accuracy digital twin modeling.

Abstract

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.