Etudiant :Jun QIAN
Début :1er mars 2012
Financement :hèse avec bourse CIFRE et Acsysteme
Date de la soutenance :26/03/2015
Commentaire :
For online parameter identification, the developed methods here allow to
design online and in closed loop optimal inputs that enrich the
information in the current experience. These methods are based on
real-time measurements of the process, on a dynamic nonlinear (or linear)
multi-variable model, on a sensitivity model of measurements with respect
to the parameters to be estimated and a nonlinear observer. Analysis of
observability and predictive control techniques are used to define the
optimal control which is determined online by constrained optimization.
Stabilization aspects are also studied (by adding fictitious constraints
or by a Lyapunov technique). Finally, for the particular case of a first
order linear system, the explicit control law is developed. Illustrative
examples are processed via the ODOE4OPE software : a bio-reactor, a
continuous stirred tank reactor and a delta wing. These examples help to
see that the parameter estimation can be performed with good accuracy in a
single and less costly experiment.
Keywords: Online closed loop parameter identification, model based
predictive control, observer, design of optimal experiment, observability,
sensitivity model.
- Auteur : Jun Qian
- Encadrement : Pascal Dufour, Madiha Nadri
- Durée d’encadrement : 1er mars 2012- mars 2015
- Financement : Thèse avec bourse CIFRE et Acsysteme
