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

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 ۲۴.۳% and a false alarm rate of only ۱.۸%, 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 ۱۴.۱۲% compared to traditional PID controllers. Simulation results in a robotic teleoperation testbed demonstrate a ۲۴.۴% improvement in trajectory tracking accuracy under variable delay conditions, ensuring precise and stable operation.

کلیدواژه ها:

Graph Neural Network ، Digital Twin ، Cybersecurity ، Smart Microgrid ، Cyber Attack Detection