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- W2900508977 abstract "In this paper, a supervised clustering based-heuristic is proposed for the real-time implementation of approximate solutions to stochastic nonlinear model predictive control frameworks. The key idea is to update on-line a low cardinality set of uncertainty vectors to be used in the expression of the stochastic cost and constraints. These vectors are the centers of uncertainty clusters that are built using the optimal control sequences, cost and constraints indicators as supervision labels. The use of a moving clustering data buffer which accumulates recent past computations enables to reduce the computational burden per sampling period while making available at each period a relevant amount of samples for the clustering task. A relevant example is given to illustrate the contribution and the associated algorithms." @default.
- W2900508977 created "2018-11-29" @default.
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- W2900508977 date "2018-11-22" @default.
- W2900508977 modified "2023-09-25" @default.
- W2900508977 title "On the use of supervised clustering in stochastic NMPC design" @default.
- W2900508977 hasPublicationYear "2018" @default.
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