An Insulin Infusion Fuzzy Controller with State Estimation for Artificial Pancreas Systems

[ES] Se propone la utilización de un controlador difuso sobre un modelo de estados mínimos con el fin de alcanzar un control de infusión de insulina continuo y eficiente en pacientes con T1DM. El sistema se apoya con un Filtro Extendido de Kalman para suplir las deficiencias de los dispositivos físicos actuales y estimar insulina remanente en el organismo con el fin de predecir su comportamiento futuro. El controlador sintonizado logra una respuesta restringida entre [80, 140] mgdl, con una media de 117, 6 mgdl y desviación estándar de 11, 3 mgdl sobre un conjunto de 365 realizaciones de 24 horas de control con 4 ingestas diarias. Estos resultados muestran que es posible diseñar controladores de baja complejidad que son fácilmente sintonizables por usuarios experimentados o médicos, con un nuevo enfoque de revisión en lazo cerrado. Además, la combinación de técnicas heurísticas con aquellas basadas en modelos permite sintentizar un controlador robusto frente al contexto real de aplicación y, también, administrar en forma eficiente el gasto de insulina. Aún así, la aplicacioón de un sistema completamente automatizado en un ser humano requerirá modelos de mayor dimensión para ajustarse a diferentes situaciones, un controlador de alta robustez y amplia adaptabilidad al organismo de cada paciente y su rutina de ingestas.[EN] A fuzzy controller for a minimal states model is proposed to achieve a continuous and effcient insulin infusion in patients with Type 1 Diabetes. An Extended Kalman Filter is also applied to supply the deficiencies of the current glucose sensor technologies and estimate residual insulin in the system to predict future behavior. The controller is tuned manually and iteratively, and achieves closed-loop responses of glycemia constrained between [80,140] (mgdl), with a mean of 117, 6 (mgdl) and a standard deviation of 11, 3 (mgdl) over a whole year ensemble of 24-hour system responses with 4 meal intakes per day. These results show that is possible to design low complexity controllers that are easily tunable by experienced users or physicians focusing on a closed-loop system response analysis. Also, the combination of heuristic and model-based techniques allows to synthesize robust controllers for real application situations and, furthermore, effciently manage the insulin usage. Nevertheless, the actual application of a closed-loop system on a human being should require higher dimension models to fit different situations, a proven robust controller and wide adaptability to different patients and their meal intake routine.Se agradece al programa Fondecyt por financiar esta investigacion dentro del marco de su proyecto 1120047González, R.; Cipriano, A. (2016). Control Difuso con Estimador de Estados para Sistemas de Páncreas Artificial. Revista Iberoamericana de Automática e Informática industrial. 13(4):393-402. https://doi.org/10.1016/j.riai.2016.09.001393402134Al-Fandi, M., Jaradat, M. A., Sardahi, Y., 2012. Optimal pid-fuzzy logic controller for type 1 diabetic patients. En: Mechatronics and its Applications (ISMA), 2012 8th International Symposium on. IEEE, pp. 1-7.Atlas, E., Nimri, R., Miller, S., Grunberg, E. A., Phillip, M., 2010. Md-logic artificial pancreas system a pilot study in adults with type 1 diabetes. 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