Diseño de un sistema de control avanzado para regular la velocidad de una turbina de vapor acoplada a un generador DC

The paper presents the methodology for the design of two control strategies, LQG (Linear-Quadratic-Gaussian Control) and DMPC (Discrete Time Model Predictive Control), for speed control in a steam turbine coupled to separately excited DC generator. The dynamics of the system is represented by a linear model in which the model parameters are calculated using an optimization algorithm. The control strategies were implemented on a distributed control system (DCS), Delta V. The goal is to maintain the speed constant despite the variation of pressure in the steam pipeline and changes in the field resistance of the DC generator.El artículo presenta la metodología para el diseño de dos estrategias de control, LQG (Linear Quadratic Gaussian Control) y DMPC (Discrete Time Model Predictive Control), para regular la velocidad de una turbina de vapor acoplada a un generador DC de excitación independiente. La dinámica del sistema es representada por un modelo lineal en el cual los parámetros del modelo se calculan a partir de un algoritmo de optimización. Las estrategias de control se implementan en el sistema de control distribuido (DCS), Delta V. El objetivo es mantener constante la velocidad ante variaciones de la presión en la tubería de vapor y modificación de la resistencia del bobinado de campo del generador DC

Analysis of the cardiorespiratory pattern of patients undergoing weaning using artificial intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.Peer ReviewedPostprint (published version

Diseño de un sistema de control avanzado para regular la velocidad de una turbina de vapor acoplada a un generador DC

The paper presents the methodology for the design of two control strategies, LQG (Linear-Quadratic-Gaussian Control) and DMPC (Discrete Time Model Predictive Control), for speed control in a steam turbine coupled to separately excited DC generator. The dynamics of the system is represented by a linear model in which the model parameters are calculated using an optimization algorithm. The control strategies were implemented on a distributed control system (DCS), Delta V. The goal is to maintain the speed constant despite the variation of pressure in the steam pipeline and changes in the field resistance of the DC generator.El artículo presenta la metodología para el diseño de dos estrategias de control, LQG (Linear Quadratic Gaussian Control) y DMPC (Discrete Time Model Predictive Control), para regular la velocidad de una turbina de vapor acoplada a un generador DC de excitación independiente. La dinámica del sistema es representada por un modelo lineal en el cual los parámetros del modelo se calculan a partir de un algoritmo de optimización. Las estrategias de control se implementan en el sistema de control distribuido (DCS), Delta V. El objetivo es mantener constante la velocidad ante variaciones de la presión en la tubería de vapor y modificación de la resistencia del bobinado de campo del generador DC

Control PID de altura de un Quadrotor

This paper contains the development of identification with the black box method and PID control of the lifting process (on the Y axis) of a model Quadrotor GAUI 330X, l The process involves developing a speed control loop angular of the motor and the sensor used was a CNZ1120 with its respective voltage-frequency converter. it is applied nonparametric methods Eyeball (Smith) and the parameters for model identification and PID closed loop control, the main objective of the research is ensure the desired speed at steady state, as a short settling time and an analysis of how to replicate this driver for each other achieve the lift

Distributed control of job-shop systems via edge reversal dynamics for automated guided vehicles

International audienceFlexible Manufacturing Systems (FMS), in which the use of Automatically Guided Vehicles (AGVs) is typical, are a growing trend in many industrial scenarios. A novel, distributed, algorithmic approach to the execution control of activities (work-center oriented) is introduced in this paper, as is, in an integrated way, transportation (AGV oriented) scheduling. The relationship between jobs, modeled as processes, and work centers, modeled as resources, and sinks defines an undirected graph G representing a target Job-shop system. Analogously, the transportation performed by AGVs, also modeled as processes, and their corresponding physical paths, modeled as resources, can also be seen as a dual Job-shop problem. The new approach is based on the Scheduling by Edge Reversal (SER) graph dynamics which, from an initial acyclic orientation over edges, that can be defined via traditional and/or efficient heuristics, let jobs and AGVs proceed in a deadlock-and-starvation-free fashion without the need for any central coordination

Analysis of cardiorespiratory interaction in patients submitted to the T-tube test in the weaning process implementing symbolic dynamics and neural networks

The determination of the optimal time of the patients in weaning trial process from Mechanical Ventilation (MV), between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Symbolic Dynamic (SD) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. In order to reduce the dimensionality of the system Forward Selection is implemented, obtaining a classification performance result of 85,96 ±6,26% with 64 variables differentiating between 3 classes analyzed at same time. © 2018 IEEE.Peer ReviewedPostprint (published version