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UID:644@cran.univ-lorraine.fr
DTSTART;TZID=Europe/Paris:20250325T090000
DTEND;TZID=Europe/Paris:20250325T120000
DTSTAMP:20250320T141812Z
URL:https://www.cran.univ-lorraine.fr/events/these-alain-uwadukunze/
SUMMARY:Thèse Alain Uwadukunze
DESCRIPTION:Titre de la thèse : System performance optimization based on i
 dentified models: application to projectile design and feed-forward contro
 l of Wiener Systems\n\nRapporteurs :\n- Stéphane Victor\, Université de 
 Bordeaux\n- Jonathan Weber\, Université de haute-Alsace\nExaminateur :\n-
  Guillaume Mercère\, Université de Poitiers\nDirecteurs et co-encadrante
  de thèse :\n- Xavier Bombois\, EC Lyon\n- Marie Albisser\, Institut Sain
 t-Louis\n- Marion Gilson\, Université de Lorraine\n\nRésumé :\nIn many 
 engineering applications\, the objective is to find the optimum performanc
 e of a system. To evaluate the performance\, it is necessary to measure th
 e output of the system for given inputs. However\, in several real-life sc
 enarios\, systems are often expensive to evaluate making it difficult to p
 erform the optimization tasks. To address this issue\, data-driven models 
 are often identified to estimate the expensive objective functions\, assoc
 iated with the systems\, and are employed to approximate their optimum. If
  poor performances are obtained using these models\, they must be improved
  by re-identifying them with new data. However\, since the systems are exp
 ensive to evaluate\, the data must be chosen carefully. The aim of this th
 esis is to develop approaches which can be used to improve identified mode
 ls employed in system performance optimization. These approaches are appli
 ed in two different applications. The first one is the aerodynamic design 
 where the goal is to find the optimum dimensions of a projectile based on 
 criteria associated with aerodynamic coefficients. These coefficients are 
 costly to acquire\, hence the projectile geometry configurations to evalua
 te\, to find the optimum\, must be selected with care. This is usually ach
 ieved using approaches such as Bayesian Optimization where a Gaussian Proc
 ess model is employed to model the static relationship between the project
 ile configuration and the objective function. In this thesis\, a procedure
  similar to Bayesian Optimization but where Neural Networks are employed a
 s data-driven models instead of Gaussian Processes is developed\, to enabl
 e scalability for larger datasets. Both approaches are used to solve the a
 erodynamic design problem\, and it is shown that they allow to reduce the 
 costs associated to aerodynamic optimization. The second application conce
 rns control engineering and more particularly the framework of identificat
 ion for control. The focus is on feed-forward controller design for non-li
 near systems which can be represented by Wiener structures. More particula
 rly\, it is shown how a model of such systems can be used to design the co
 ntroller. A procedure to iteratively improve the model and re-design the c
 ontroller is also introduced in the case where the initially designed one 
 does not allow to obtain optimal performances. Overall\, the developed app
 roaches provide effective solutions to minimizing system evaluation costs 
 during optimization tasks in diverse engineering fields\n\n
LOCATION:Polytech Nancy\, 2 rue Jean Lamour\, Vandœuvre-lès-Nancy\, 54519
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