An ESN based Modeling for Roll-to-Roll Printing Systems
Zhihua Chen, Tao Zhang, Zheng Zhang · 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) · 2020
In this paper, a modeling scheme based on Echo State Networks (ESN) is designed and discussed for modeling in Roll-to-Roll (R2R) systems. R2R system involves transport and process of thin, flexible, continuous materials (called webs). An accuracy model is critical to the research of R2R system, such as model-based control and prediction. Existing mechanism modeling methods currently used in R2R systems require complex derivation and do not provide the accuracy performance for changing operating conditions and material properties. The modeling scheme based on ESN utilizes the nonlinear approximation approach where the optimal output connect weights of the network are calculated based on matching of the actual closed-loop R2R printing system. The model established by the proposed method considers the effect of operating conditions and material properties. Experimental data from an industrial printing system is used to corroborate the accuracy of R2R system model can be raised double by the proposed method which compared with mechanism modeling methods.