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Theses

Data informativity for the prediction error identification of MIMO systems : identification of a MEMS gyroscope

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.
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Kévin Colin. Data informativity for the prediction error identification of MIMO systems : identification of a MEMS gyroscope. Other. Université de Lyon, 2020. English. ⟨NNT : 2020LYSEC018⟩. ⟨tel-03114994⟩

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