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Human-inspired algorithms for designing new control system in the context of factory of the future

Abstract : The use of traditionally centralized control system does not able to meet the rapidly changing customer expectations, high product varieties, and shorter product life-cycles. In particular, the emergence of Cyber Physical System (CPS) which can be seen as interacting networks of physical and computational components has provided the foundation for many new factories’ infrastructures and improved the quality of products and processes. This Cyber Physical System has dramatically impacted the centrally predictive control system in responding to perturbation(s) in the current dynamic market characteristics. Urgent change for example is one of the common perturbations and has significant perturbing ability to a central predictive control system. Accordingly, it is now accepted that using agent-based control system improves the reactivity to treat these perturbation(s) until they are no longer limiting factors. In this study, the use of human-inspired interaction approach (by means of negotiation and consensus-based decision-making algorithms) is explored to design and propose a new control system. It has taken advantages from Industry 4.0 assets and encompassed heterogeneous and intelligent entities (mainly the product entities and resource entities) and discrete event systems. Each entity could have different capability (evolution, learning, etc.) and the whole physical and control system may lead emerging behaviors to dynamically adapt the perturbation(s). Hence, every intelligent entity decides when to broadcast its current state to neighbor entities and the controlling decision depends on the behavior of this state. The negotiation and consensus-based decision-making algorithms were initially formulated and modeled by networking all the contributing and heterogeneous entities considering two real industrial shop floors. Then after, simulation and application implementation tests on the basis of full-sized academic platform called TRACILOGIS platform have been conducted to verify and validate these decision-making algorithms. This has been done with expectations that the applicability of these algorithms will be more adaptable to set best priority-based product sequencing and rescheduling than another decision-making approach called pure reactive control approach. Accordingly, the experimental results have shown that the negotiation and consensus among the decisional entities have significantly minimized the impact of perturbation(s) on a production process launched on the TRACILOGIS platform. Meanwhile, using these two decision-making algorithms has conveyed better global performance (e.g., minimized makespan) over the pure reactive control approach.
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https://hal.univ-lorraine.fr/tel-02510677
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Tsegay Tesfay Mezgebe. Human-inspired algorithms for designing new control system in the context of factory of the future. Automatic. Université de Lorraine, 2020. English. ⟨NNT : 2020LORR0006⟩. ⟨tel-02510677⟩

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