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On the use of Data-Driven Cost Function Identification in Parametrized NMPC

Mazen Alamir 1
1 GIPSA-MODUS - GIPSA - Modelling and Optimal Decision for Uncertain Systems
GIPSA-PAD - GIPSA Pôle Automatique et Diagnostic
Abstract : In this paper, a framework with complete numerical investigation is proposed regarding the feasibility of constrained Nonlinear Model Predictive Control (NMPC) design using Data-Driven model of the cost function. Although the idea is very much in the air, this paper proposes a complete implementation using python modules that are made freely available on a GitHub repository. Moreover, a discussion regarding the different ways of deriving control via data-driven modeling is proposed that can be of interest to practitioners.
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https://hal.archives-ouvertes.fr/hal-02569083
Contributor : Mazen Alamir <>
Submitted on : Monday, May 11, 2020 - 8:02:58 AM
Last modification on : Wednesday, October 21, 2020 - 3:14:59 AM

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  • HAL Id : hal-02569083, version 1
  • ARXIV : 2005.04076

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Mazen Alamir. On the use of Data-Driven Cost Function Identification in Parametrized NMPC. 2020. ⟨hal-02569083⟩

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