Thesis Defense of GONZALEZ DE COSSIO FRANCISCO 5th of December

5th of December 2019, 10 am “bibliothèque de la doua”

Jury :

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)


Estimating the state of a nonlinear system is an essential task for achieving important objectives such as: process monitoring, identification and control. Observers are algorithms that estimate the current state by using, among other information, sensor measurements. Recently, there has been an increasing interest in the design of observers for more realistic models, which can include disturbances, sensor nonlinearities and discrete outputs. We can distinguish two main parts in our work. The first part concerns state-affine systems affected by noise and studies the state estimation via the so-called high-gain Kalman filter. We present a new optimization algorithm, based on Lyapunov analyses, that adapts the tuning parameter and the system input in order to minimize the effect of both dynamic and output 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 and, thus, such an output mismatch needs to be addressed. Our observer redesign consists in the interconnection of the original observer with an output estimator based on a dynamic inversion, and we show its asymptotic convergence via small-gain arguments. Finally, we extend our redesign method for systems with outputs that are both transformed and discretized in time. We implement sample-and-hold techniques leading to an observer gain based on LMI’s. The main feature of our redesign methods is the possibility to adapt a large number of observers from the literature to more realistic scenarios.



Nonlinear systems – nonlinear sensors – discrete measurements – noise robustness – nonlinear observers – high-gain observers – interconnected observers – Kalman filter – Lyapunov stability – input-to-state stability – small-gain – linear matrix inequalities

Date(s) - 5 Dec 2019
10 h 00 min

Bibliothèque de la Doua


Filed under: Defense, DYCOP