Graph Neural Network-Based Digital Twin for Cyber-Resilient and Predictive Teleoperation Systems

Mahmoudi Rashid, Sara

doi:10.22044/jadm.2025.16223.2747

Articles in Press

Document Type : Original/Review Paper

Author

sara Mahmoudi Rashid

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

10.22044/jadm.2025.16223.2747

Abstract

Teleoperation systems are increasingly deployed in critical applications such as robotic surgery, industrial automation, and hazardous environment exploration. However, these systems are highly susceptible to network-induced delays, cyber-attacks, and system uncertainties, which can degrade performance and compromise safety. This paper proposes a Graph Neural Network (GNN)-based Digital Twin (DT) framework to enhance the cyber-resilience and predictive control of teleoperation systems. The GNN-based anomaly detection mechanism accurately identifies cyber-attacks, such as false data injection (FDI) and denial-of-service (DoS) attacks, with a detection rate of 24.3% and a false alarm rate of only 1.8%, significantly outperforming conventional machine learning methods. Furthermore, the predictive digital twin model, integrated with model predictive control (MPC), effectively compensates for latency and dynamic uncertainties, reducing control errors by 14.12% compared to traditional PID controllers. Simulation results in a robotic teleoperation testbed demonstrate a 24.4% improvement in trajectory tracking accuracy under variable delay conditions, ensuring precise and stable operation.

Keywords

Main Subjects

H.3. Artificial Intelligence

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