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• W4285295448 abstract "An approach to safely learn and deploy, at fast rate, a given optimization-based controller for the routing problem in smart manufacturing is presented. The considered application features a large number of integer decision variables, combined with nonlinear dynamics, temporal-logic constraints, and hard safety constraints. The approach employs a neural network as feedback controller, trained using a data-set of state-input pairs collected with the optimization-based controller. A safeguard mechanism checks whether the input computed by the neural network is feasible or not, in the latter case the optimization-based controller is called. Results on a high-fidelity simulation suite indicate a strong decrease of average computational time combined with a negligible loss of plant performance." @default.
• W4285295448 created "2022-07-14" @default.
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• W4285295448 date "2022-01-01" @default.
• W4285295448 modified "2023-10-18" @default.
• W4285295448 title "Safeguarded optimal policy learning for a smart discrete manufacturing plant" @default.
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• W4285295448 doi "https://doi.org/10.1016/j.ifacol.2022.04.226" @default.
• W4285295448 hasPublicationYear "2022" @default.
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