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Home > Thèses et HDR > Thèses en 2020

17/09/2020 - Kévin COLIN

by Laurent Krähenbühl - published on , updated on


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Kévin Colin defends his PhD on Sept. 17, 2020 at 2:00 PM.
Place : Ecole Centrale de Lyon, Bâtiment W1, visio conference only.

Title : Data Informativity for the Prediction Error Identification of MIMO Systems. Identification of a MEMS Gyroscope

Jury :
Rapporteurs :
- M. Johan Schoukens, Professeur, Vrije Universiteit Brussels
- M. Guillaume Mercère, Maître de Conférence (HdR), Université de Poitiers (LIAS)
Autres membres :
- Mme Marion Gilson, Professeure, Université de Lorraine (CRAN)
- M. Hakan Hjalmarsson, Professeur, KTH Royal Institute of Technology
- M. Xavier Bombois, Directeur de Recherche CNRS, Ecole Centrale de Lyon (Ampère)
- M. Laurent Bako, Maître de Conférence (HdR), Ecole Centrale de Lyon (Ampère)

Abstract :
Mathematical models have a crucial place in every engineering field. They can be used for several purposes such as the design of a controller, the prediction, the health monitoring of a system, etc. In this thesis, we deal with system identification which is the scientific field consisting in the modeling of a system with experimental data. More particularly, we will consider the Prediction Error method. In order to get an accurate identified model, the data must guarantee one fundamental property which is the informativity. The data informativity has been largely studied for the identification of linear single-input single-output systems. However, few results can be found for the identification of linear multiple-inputs multiple-outputs (MIMO) systems. This is inconvenient since the systems get more and more complex. Hence, in the first part of this thesis, we focus on developing new conditions to verify the data informativity for the open-loop and closed-loop identification of linear MIMO systems.
However, most of real-life systems have nonlinear dynamics. Fortunately, Prediction Error identification can be used as an efficient tool for the modeling of some classes of nonlinear systems such as Hammerstein systems, i.e., systems where the nonlinearity is found at the input of the system. In this thesis, we study a particular class of Hammerstein systems. The motivation of this study comes from the real-life considered in this thesis : the MEMS gyroscope.
A MEMS gyroscope is a micro-sensor that measures angular rates. It has several advantages such as its small size, its low energy consumption and its cheap price. However, it is less accurate than its optical counterpart. In order to tackle this accuracy issue, the MEMS gyroscope is put in closed-loop. Of course, we want to design an optimal controller. For this purpose, we need to derive an accurate model of the dynamics of the MEMS gyroscope. In the literature, the proposed models are not enough complete. Therefore, in this thesis, we develop an identification method that yields an accurate and complete model of the dynamics of the MEMS gyroscope. We observe that the previous study of the data informativity can be applied to this real-life problem.

Key Words:
Prediction Error identification; consistency analysis; MIMO systems; data informativity; Hammerstein system identification; MEMS gyroscope

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