Online Model-Based Redesign of Experiment (OMBRE) strategies represent a valuable support to the development of dynamic deterministic models, allowing for the dynamic update of the experimental conditions to yield the most informative data for the parameter identification task. However, the effectiveness of OMBRE strategies may be severely affected by the presence of systematic modelling errors. In this paper, a disturbance estimation approach is exploited within an OMBRE framework (DEOMBRE) in order to achieve a statistically satisfactory estimation of the model parameters, thus avoiding (or reducing) constraint violations even in the presence of systematic modelling errors. A case study illustrates the benefits of the new approach. © 2011 Elsevier B.V.
A Disturbance Estimation Approach for Online Model-based Redesign of Experiments in the Presence of Systematic Errors
PANNOCCHIA, GABRIELE;
2011-01-01
Abstract
Online Model-Based Redesign of Experiment (OMBRE) strategies represent a valuable support to the development of dynamic deterministic models, allowing for the dynamic update of the experimental conditions to yield the most informative data for the parameter identification task. However, the effectiveness of OMBRE strategies may be severely affected by the presence of systematic modelling errors. In this paper, a disturbance estimation approach is exploited within an OMBRE framework (DEOMBRE) in order to achieve a statistically satisfactory estimation of the model parameters, thus avoiding (or reducing) constraint violations even in the presence of systematic modelling errors. A case study illustrates the benefits of the new approach. © 2011 Elsevier B.V.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
