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- W1546077860 abstract "Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses or nonlinear discrete-time models. Previously, we have extended MPC to a class of discrete event systems that can be described by a model that is linear in the max-plus algebra. In our previous work we have considered MPC for the perturbations-free case and for the case with noise and/or modeling errors in a bounded or stochastic setting. In this paper we consider a method to reduce the computational complexity of the resulting optimization problem, based on variability expansion. We show that the computational load is reduced if we decrease the level of 'randomness' in the system." @default.
- W1546077860 created "2016-06-24" @default.
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- W1546077860 date "2003-08-27" @default.
- W1546077860 modified "2023-09-25" @default.
- W1546077860 title "Complexity reduction in MPC for stochastic max-plus-linear systems by variability expansion" @default.
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- W1546077860 doi "https://doi.org/10.1109/cdc.2002.1184430" @default.
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