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A Simple Machine Learning Technique for Model Predictive Control
A Simple Machine Learning Technique for Model Predictive Control
Abstract : This paper is devoted to a simple approach for the offline computation of closed-loop optimal control for dynamical systems with imposed terminal state arising in Model Predictive Control Scheme (MPC). The here-proposed approach simply relies on some integrations of the characteristic equations associated to the optimal control problem, together with the classical supervised learning of a one-hidden-layer neuron network, to get a closed-loop MPC completely computed offline. Some examples are provided in the paper, which demonstrate the ability of this approach to tackle some quite large problems, with state dimensions reaching 50, without encountering limitations due to the so-called curse of dimensionality.
Cited literature [14 references]
https://hal.archives-ouvertes.fr/hal-02179706
Contributor : Didier GEORGES Connect in order to contact the contributor
Submitted on : Thursday, July 11, 2019 - 10:15:44 AM
Last modification on : Monday, January 24, 2022 - 5:09:31 PM
Contributor : Didier GEORGES Connect in order to contact the contributor
Submitted on : Thursday, July 11, 2019 - 10:15:44 AM
Last modification on : Monday, January 24, 2022 - 5:09:31 PM
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paper_MED2019_DGeorges.pdf
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