5th of December 2019, 10 am “bibliothèque de la doua”
Rapporteur Sergey Dashevsky (University of Würzburg, Germany)
Rapporteur Alain Rapaport (Mistea/INRA, France)
Examinateur Dragan Nesic (University of Melbourne, Australia)
Examinateur Pauline Bernard (CAS/MINES ParisTech, France)
Examinateur Bernhard Maschke (Lagepp/Université Claude Bernard, France)
Examinateur Vincent Andrieu (Lagepp/Université Claude Bernard, France)
Co-directeur Pascal Dufour (Lagepp/Université Claude Bernard, France)
Co-directeur Madiha Nadri (Lagepp/Université Claude Bernard, France)
Abstract: This thesis concerns the design of robust observers for nonlinear systems and we can distinguish two parts:
The first part studies state-affine systems affected by noise, and analyses the state estimation via the high-gain Kalman filter. We present a new optimization algorithm that adapts the tuning parameter of the observer and the system input in order to minimize the effect of disturbances.
The second part studies the problem of observer redesign for general nonlinear systems whose outputs are transformed by a nonlinear function. Indeed, a given observer might not estimate the system state properly if it does not take into account sensor nonlinearities. We present a robust observer redesign that tackles this problem and we provide a proof of its asymptotic convergence. Finally, we extend our redesign method to system outputs that are not only transformed but also discretized in time. The main feature of our redesign methods is the possibility to adapt a large number of observers from the literature to more realistic scenarios.
Keywords: nonlinear dynamical systems – nonlinear sensors – discrete measurements – noise robustness – nonlinear observers – high-gain observers – Kalman filter – Lyapunov stability – input-to-state stability
Date(s) - 5 Dec 2019
10 h 00 min
CatégoriesFiled under: Defense