Learning Transformed Dynamics for Efficient Control Purposes

Chady Ghnatios (), Joel Mouterde, Jerome Tomezyk, Joaquim Da Silva and Francisco Chinesta
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Chady Ghnatios: PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
Joel Mouterde: SKF Magnetic Mechatronic, 27950 Saint-Marcel, France
Jerome Tomezyk: SKF Magnetic Mechatronic, 27950 Saint-Marcel, France
Joaquim Da Silva: SKF Magnetic Mechatronic, 27950 Saint-Marcel, France
Francisco Chinesta: PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France

Mathematics, 2024, vol. 12, issue 14, 1-22

Abstract: Learning linear and nonlinear dynamical systems from available data is a timely topic in scientific machine learning. Learning must be performed while enforcing the numerical stability of the learned model, the existing knowledge within an informed or augmented setting, or by taking into account the multiscale dynamics—for both linear and nonlinear dynamics. However, when the final objective of such a learned dynamical system is to be used for control purposes, learning transformed dynamics can be advantageous. Therefore, many alternatives exists, and the present paper focuses on two of them: the first based on the discovery and use of the so-called flat control and the second one based on the use of the Koopman theory. The main contributions when addressing the first is the discovery of the flat output transformation by using an original neural framework. Moreover, when using the Koopman theory, this paper proposes an original procedure for learning parametric dynamics in the latent space, which is of particular interest in control-based engineering applications.

Keywords: dynamical systems; flat control; machine learning; nonlinear control; Koopman theory; parametric dynamics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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