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A Simple Machine Learning Technique for Model Predictive Control

Didier Georges 1 
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.
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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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Didier Georges. A Simple Machine Learning Technique for Model Predictive Control. MED 2019 - 27th Mediterranean Conference on Control and Automation, Jul 2019, Akko, Israel. ⟨10.1109/MED.2019.8798512⟩. ⟨hal-02179706⟩

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