Safeguarded optimal policy learning for a smart discrete manufacturing plant. Issue 2 (2022) - Boffadossi, Roberto; Bonassi, Fabio; Fagiano, Lorenzo; Scattolini, Riccardo; Cataldo, Andrea
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.