Parameter uncertainties characterization for linear models

José Ragot (1) , Didier Maquin (1) , Olivier Adrot (2)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 LAG - Laboratoire d'automatique de Grenoble

Parameter estimation mainly consists in characterising a parameter set consistent with measurements, the model and the equation error description. The problem to be solved is that of finding the set of admissible parameter values corresponding to an admissible error. The uncertainties must be treated by a global analysis of the problem: both the equation error and the parameter set are considered unknown. Then, a solution is given as a domain of time-variant parameters and a bounded set of the error. This procedure consists in explaining the measurements performed at all time by optimising a precision criterion based on the polytope theory.

Mots clés: parameter estimation; bounded error; characterization; uncertain model; set-membership approach; uncertainties; parameter estimation.

Domaines: Automatique

José Ragot, Didier Maquin, Olivier Adrot. Parameter uncertainties characterization for linear models. 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SafeProcess 2006, Aug 2006, Beijing, China. ⟨hal-01061513⟩