Diseño de un regulador no lineal por bloques para el sistema glucosa-insulina utilizando redes neuronales de alto orden

En este trabajo se propone un algoritmo de control en lazo cerrado para el control automático de la diabetes tipo 1 basado en la identificación de sistemas no lineales con redes neuronales artificiales y mediante la regulación basada en la forma controlable no lineal por bloques. Como paciente virtual se utiliza el modelo de Hovorka al que se conecta el algoritmo de control utilizando prealimentación procedente de la terapia prescrita con insulina y un módulo de seguridad para evitar las hipoglucemias. El identificador neuronal es entrenado en línea con un filtro de Kalman extendido con una función de activación definida por la tangente hiperbólica. El controlador no lineal por bloques se basa en la estructura de la red neuronal, cuya salida es la propuesta de dosificación de insulina antes de prealimentación y módulo de seguridad. El algoritmo presenta un peso que se interpreta como una ganancia de controlabilidad. La glucosa del paciente está condicionada al valor de la ganancia, se definen tres ensayos con diferentes valores: ensayo A (10?3); ensayo B (7 · 10?4) y ensayo C (9 · 10?3). El valor del peso de controlabilidad condiciona la cantidad de insulina propuesta por el controlador de forma directa en el paciente virtual medio del modelo de Hovorka

Glucose-Insulin regulator for type 1 diabetes using high order neural networks

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas? beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed- Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feedforward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period

Linear closed-loop control using Luenberger observer applied to insulin administration for type 1 diabetes

In this work the design of a Luenberger observer is proposed to estimate the unmeasurable state space variables from Hovorka?s model. This model is linearized and evaluated in an operation point where Luenberger observer is designed using the Ackermann methodology. The observer is employed to estimate the unmeasurable variables of virtual patients which are generated by Bergman?s model. Once the unmeasurable state variables are obtained by the Luenberger observer using only the input-output information of the Bergman?s model, a control algorithm based on eigenvalues relocation trough Ackermann methodology for linear systems is applied. In this methodology, a constant feedback gain vector is obtained in order to compute the control signal (insulin) to be applied to virtual patient and keep on normoglycemia rank. The carbohydrates ingestion is considered as the main disturbances. In order to assess the proposed methodology, two tests are designed: the first one consists of changing the reference signal in order to evaluate the control sensitivity; and the second one includes different proportions of prandial insulin used in open-loop to try the controller response under distinct operation conditions. The results are obtained via simulation using Simulink of Matlab