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Paper detail

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

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.

preprint2020arXivOpen access
Mazen Alamir
Systems and Controleess.SYmath.OCMachine Learning
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Mazen Alamir

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