Delay Safety-Aware Digital Twin Empowered Industrial Sensing-Actuation Systems Using Transferable and Reinforced Learning

The industrial visual sensing-actuation system is an implementation approach to construct the loop between the digital twin and physical systems, which is facing the following challenges. First, the cross-digital-physical information exchanges bring a high end-to-end delay that threatens the functional safety of industrial systems. Second, industrial scenarios are diverse, such as manufacturing, chemical engineering, etc., which makes the intelligent sensing strategies for one scenario inapplicable to others, especially for few-shot cases. Third, intelligent actuation strategies cannot allocate resources across digital and physical domains. We propose the delay-minimization-based intelligent digital twin approach to address the above challenges. The digital twin framework incorporates samples from the physical domain to train the learning models in the digital domain. The proposed scheme tailors and adapts transferable and reinforced learning models with end-to-end delay analysis to optimize the training process. The feasibility and efficiency of the scheme are validated by simulations.