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Habilitation à diriger des recherches

From robust estimation to hybrid system identification

Abstract : This report elaborates on my research activities with a particular focus put on the period that ranges from my graduation until now (2009-2016). Only a brief overview of scientific results obtained on this period is presented. The research topic discussed is mainly concerned with hybrid system identification from input-output measurements. Hybrid systems form a class of dynamic systems where discrete and continuous dynamics interact. The global behavior results from switching among a finite number of subsystems. A fundamental challenge associated with the identification of such systems is that the available data points are not labelled beforehand in the sense that one does not know a priori which data point is generated by which subsystem. Ideally, one would like to partition the data points into a finite number of groups each of which is relevant to a single subsystem. However this is typically a nonconvex procedure which does not admit any numerically efficient solution. We propose a robust identification approach whose principle is to fit appropriately the entire mixed dataset to a single equation. A common thread of our results is the concept of sparse optimization with its associated convex relaxations. This common idea is presented in the third chapter as a solution to the robust regression problem. It is later applied to the identification of switched linear systems and piecewise affine systems.
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Habilitation à diriger des recherches
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Submitted on : Wednesday, June 7, 2017 - 7:30:25 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:46 PM
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Laurent Bako. From robust estimation to hybrid system identification. Automatic. Université de Lyon - Ecole Centrale de Lyon, 2016. ⟨tel-01534618⟩

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