LAUSR

In the new generation of industrial cyber-physical system (ICPS), data-driven control is one of the emerging intelligent control methods to realize efficient production adjustment. In most existing works, the perfect sensing process is regarded as the fundamental assumption. However the experienced sensing strategies deployed in advance are increasingly difficult to adapt to the expanding network scale and diversified production demands in the Industry 4.0 era. To tackle the challenges, we propose the novel intelligent edge sensing and control co-design (IESCC) framework under ICPS. The cooperation of five constructed graph convolutional neural networks respectively related to system model, sensing model, estimator, actor and critic is adopted to approximate the coupled optimality conditions of sensing and control strategies. The structure of learning networks is designed in advance for online strategy solving tailored for the real-time industrial requirements and edge computing power. In particular, the representation capabilities of learning networks under different scales are quantitatively analyzed from the perspectives of observability and controllability. Besides, the feasible region of learning rates is explicitly depicted to ensure convergence. Finally, the proposed algorithm is applied into the laminar cooling process in the semi-physical simulation. Compared with the state-of-the-art approaches, our method can always guarantee observability and controllability. And up to 27.9${\%}$ overall performance of sensing and control is improved, and 38${\%}$ execution time reduction is achieved on average.