Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops

2026-10-08

AI Summary

This study addresses the limitation of existing digital twins, which lack an integrated perspective encompassing physical processes, communication, and application logic, thereby hindering the validation of AI-driven industrial edge control loops. To overcome this, we propose a modular, unified digital twin architecture that jointly models physical dynamics, wireless communication, and application logic, enabling impact analysis of AI deployments across local, edge, and cloud environments. Experimental evaluations conducted on a 5G-enabled autonomous mobile robot with AI-based remote control demonstrate strong agreement between predicted and measured performance. The results reveal the critical influence of network modeling inaccuracies on latency-sensitive control loops and confirm the feasibility of the proposed framework for pre-deployment validation in industrial edge scenarios.

Abstract

Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization. However, existing DT solutions typically focus either on industrial processes or communication networks, while lacking an integrated and application-aware perspective. To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture. The feasibility of the proposed framework is experimentally validated through a real-world Proof-of-Concept (PoC) implemented in the BI-REX pilot line, involving a 5G-connected Autonomous Mobile Robot (AMR) transporting hazardous liquids and remotely controlled by an AI-driven application. The proposed DT is used to reproduce the behaviour of the real deployment and to investigate the impact of different placements of the AI application, including on-premise, edge, and remote cloud execution scenarios. Experimental results demonstrate a close agreement between DT predictions and PoC measurements in terms of both network-level metrics, such as Reference Signal Received Power (RSRP) and latency, and end- to-end application metrics, including application-level Round- Trip-Time (RTT). Moreover, the analysis shows how inaccuracies of network modeling can critically affect the feasibility of latency-sensitive industrial control loops, highlighting the potential of integrated DTs as tools for the pre-deployment design and validation of next-generation industrial systems.

Problem

• Digital Twin
• Industrial Edge Control
• Pre-Deployment Validation
• Safety-Critical Systems
• Integrated Modeling

Innovation

• Digital Twin
• Edge Computing
• Safety-Critical Control Loops
• 5G
• Pre-Deployment Validation