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Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control

Abstract : The automated conveyor system, as the core component in the modern manufacturing world, has gained lots of attention from researchers. To optimize the operation of the conveyor system, range-inspection control (RIC) has been considered an efficient strategy to bring this conventional technology to an intelligent level. Various algorithms have been put into use to achieve optimal control. However, the current methodologies are only focusing on control optimization, not scaled into the smart manufacturing framework. The schema of alignment and corporation between the physical and virtual spaces for the system remains an important problem. Therefore, the work in this article aims for an effective framework of implementation between the physical and virtual stations in an automated conveyor system. Since increasingly more application scenarios rely on the digital twin (DT) technology to realize the integration of physical and virtual systems, we proposed the DT automated conveyor system (DT-ACS) that constructs the road map to implement the RIC-based conveyor system under the background of a smart factory. Besides, profit-sharing-based deep Q-networks (PDQNs) have been proposed to cope with the RIC optimization problem. The robustness and efficiency of the proposed PDQN were evaluated via sets of experiments. The discussion and conclusion are presented at last accordingly.
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https://hal-utt.archives-ouvertes.fr/hal-03320628
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:19:28 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tian Wang, Jiaxiang Cheng, Yi Yang, Christian Esposito, Hichem Snoussi, et al.. Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control. IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2020, pp.1-9. ⟨10.1109/TASE.2020.3043393⟩. ⟨hal-03320628⟩

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