Aplicación de la ingeniería del software sobre la herramienta MATE : application controller

Este proyecto tiene como objetivo crear y aplicar una metodología a una aplicación llamada MATE que fue creada en en el año 2003 por Anna Sikora para su tesis doctoral. Se trata de dotar el proyecto MATE de las herramientas necesarias para garantizar su evolución. La metodología creada consta de la especificación de un entorno de trabajo y una serie de documentos que detallan los procesos relativos al desarrollo de MATE. Además se han creado algunas nuevas características que hacen de MATE una herramienta más completa y cómoda.

Controladores PI con acción de reset

El control automàtic exerceix un paper important en molts processos de la industria. Cada un dels sistemes de control requereix d’un controlador, la majoria dels quals són del tipus PI. L’objectiu d’aquest projecte es investigar tècniques que permetin superar les limitacions que tenen els controladors PI lineals. En la resposta d’un sistema de control es poden distingir dues tasques diferents: El seguiment a un canvi d’entrada o consigna correspon a la tasca de servo, mentre que el rebuig a pertorbacions correspon a la tasca de regulatori. Al típic esquema de control realimentat, aquestes dues tasques estan enfrontades, és a dir, una millora a la tasca de servo implica un empitjorament a la tasca de regulatori i a l’inversa. Això suposa un problema al rendiment del sistema, així com la necessitat d’establir un cert compromís entre les dues tasques. El que es pretén en aquest projecte es implementar senzilles regles de control no lineal amb la finalitat de millorar el rendiment del sistema i evitar la necessitat d’establir un compromís entre les dues tasques. Així, es pretén superar les limitacions que aquest té, obtenint controladors PI alternatius fàcilment sintetitzables.

Smith Predictor Sliding Mode Closed-loop Glucose Controller in Type 1 Diabetes

Type 1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. In this work, two robust closed-loop controllers for blood glucose regulation are developed to prevent the life-threatening hypoglycemia, as well as to avoid extended hyperglycemia. The proposed controllers are designed by using the sliding mode control technique in a Smith predictor structure. To improve meal disturbance rejection, a simple feedforward controller is added to inject meal-time insulin bolus. Simulations scenarios were used to test the controllers, and showed the controllers ability to maintain the glucose levels within the safe limits in the presence of errors in measurements, modeling and meal estimation

A Gain Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

This paper presents a control strategy for blood glucose(BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test thecontroller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and errors in mealestimation

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

The time required to image large samples is an important limiting factor in SPM-based systems. In multiprobe setups, especially when working with biological samples, this drawback can make impossible to conduct certain experiments. In this work, we present a feedfordward controller based on bang-bang and adaptive controls. The controls are based in the difference between the maximum speeds that can be used for imaging depending on the flatness of the sample zone. Topographic images of Escherichia coli bacteria samples were acquired using the implemented controllers. Results show that to go faster in the flat zones, rather than using a constant scanning speed for the whole image, speeds up the imaging process of large samples by up to a 4x factor.