Further results on plant parameter identification using continuous-time multiple-model adaptive estimators

This paper describes a deterministic approach to adaptive state and parameter estimation using a multiple model structure. In the set-up adopted, the plant of interest is described by a finite dimensional model with parametric uncertainty. To each choice of a finite number of parameter values there corresponds a finite set of multiple design models and a corresponding set of observers. Assuming the latter have been chosen, a Dynamic Weighting Signal Generator (DWSG) performs on-line adaptation of the weights given to the individual observer estimates based on the energy of the output error signals. In the present paper we develop a distance-like pseudo norm between the true plant and the identified model in a deterministic setting, based on the energy of the output error signals. Furthermore we show, under a distinguishability condition, that the model identified is the one that is closest to the true plant in the defined deterministic norm. We also prove that the convergence of the pa...

• Type: Conference
• Year: 2009
• Where: CDC
• Authors: Vahid Hassani, A. Pedro Aguiar, António Manuel Santos Pascoal, Michael Athans
• Keywords: CDC 2009, Control Systems, Finite Dimensional Model, Model, Output Error Signals