Direct model predictive control

Jean-Joseph Christophe 1, 2 Jérémie Decock 1, 2 Olivier Teytaud 1, 2
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : 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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Submitted on : Tuesday, March 11, 2014 - 6:51:43 PM
Last modification on : Monday, December 9, 2019 - 5:24:06 PM
Long-term archiving on: Wednesday, June 11, 2014 - 1:25:28 PM

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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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