Postprandial characterization of patients with type 1 diabetes is crucial for the development of an automatic glucose control system (Artificial Pancreas). Uncertainty sources within the patient, and variability of the glucose response between patients, are a challenge for individual patients model identification leading to poor predictability with current methods. Also, continuous glucose monitors, which have been the springboard for research towards a domiciliary artificial pancreas, still introduce large measurement errors, greatly complicating the characterization of the patient. In this thesis, individual model identification characterizing intra-patient variability from domiciliary data is addressed. First, literature models are reviewed. Next, we investigate the collection of data, and how can it be improved using optimal experiment design. Data gathering improvement is later applied to an ambulatory clinical protocol implemented at the Hospital Clínic Universitari de València, and data are collected from twelve patients following a set of mixed meal studies. With regard to the uncertainty of the glucose monitors, two continuous glucose monitoring devices are analyzed and statistically modeled. The models of these devices are used for in silico simulations and the analysis of identification methods. Identification using intervals models is then performed, showing an inherent capability for characterization of both the patient and the related uncertainty. First an in silico study is conducted in order to assess the feasibility of the identifications. Then, model identification is addressed from real patient data, increasing the complexity of the problem. As conclusion a new method for interval model identification is developed and successfully validated from clinical data. / Laguna Sanz, AJ. (2014). Uncertainty in Postprandial Model Identification in type 1 Diabetes [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37191 / Alfresco

The performance of a model-based diagnosis system could be affected by several uncertainty sources, such as,model errors,uncertainty in measurements, and disturbances. This uncertainty can be handled by mean of interval models.The aim of this thesis is to propose a methodology for fault detection, isolation and identification based on interval models. The methodology includes some algorithms to obtain in an automatic way the symbolic expression of the residual generators enhancing the structural isolability of the faults, in order to design the fault detection tests. These algorithms are based on the structural model of the system. The stages of fault detection, isolation, and identification are stated as constraint satisfaction problems in continuous domains and solved by means of interval based consistency techniques. The qualitative fault isolation is enhanced by a reasoning in which the signs of the symptoms are derived from analytical redundancy relations or bond graph models of the system. An initial and empirical analysis regarding the differences between interval-based and statistical-based techniques is presented in this thesis. The performance and efficiency of the contributions are illustrated through several application examples, covering different levels of complexity. / ENLas prestaciones de un sistema de diagnosis basado en modelos se pueden ver afectadas por fuentes de incertidumbre como los errores en el modelo,la incertidumbre en las medidas y las perturbaciones.Esta incertidumbre se puede tratar mediante modelos intervalares.Esta tesis propone una metodología de detección, aislamiento e identificación de fallos basada en modelos intervalares. La metodología incluye algoritmos para obtener de manera automática la expresión simbólica de los generadores de residuos mejorando la aislabilidad estructural de los fallos. Estos algoritmos se basan en el modelo estructural del sistema.Las etapas de detección, aislamiento, e identificación de fallos se representan como problemas de satisfacción de restricciones en dominios continuos y se resuelven por medio de técnicas de consistencia basadas en intervalos.Una mejora en el aislamiento cualitativo de los fallos se obtiene por razonamiento con los signos de los síntomas, que se obtienen de relaciones de redundancia analítica o de modelos bond graph del sistema.Esta tesis también presenta un análisis empírico inicial de las diferencias entre las técnicas basadas en intervalos y las basadas en técnicas estadísticas.Las prestaciones y la eficiencia de las contribuciones de la tesis se ilustran a través de unos cuantos ejemplos de aplicación, que cubren diferentes niveles de complejidad.