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Conference Papers Year : 2014

Direct model predictive control


Due to simplicity and convenience, Model Predictive Control, which consists in optimizing future decisions based on a pessimistic deterministic forecast of the random processes, is one of the main tools for stochastic control. Yet, it suffers from a large computation time, unless the tactical horizon (i.e. the number of future time steps included in the optimization) is strongly reduced, and lack of real stochasticity handling. We here propose a combination between Model Predictive Control and Direct Policy Search.
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hal-00958192 , version 1 (11-03-2014)


  • HAL Id : hal-00958192 , version 1


Jean-Joseph Christophe, Jérémie Decock, Olivier Teytaud. Direct model predictive control. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2014, Bruges, Belgium. ⟨hal-00958192⟩
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