A Nonlinear TSNN Based Model of a Lead Acid Battery

The paper studies a nonlinear model based on time series neural network system (TSNN) to improve the highly nonlinear dynamic model of an automotive lead acid cell battery. Artificial neural network (ANN) take into consideration the dynamic behavior of both input-output variables of the battery charge-discharge processes. The ANN works as a benchmark, its inputs include delays and charging/discharging current values. To train our neural network, we performed a pulse discharge on a lead acid battery to collect experimental data. Results are presented and compared with a nonlinear Hammerstein-Wiener model. The ANN and nonlinear autoregressive exogenous model (NARX) models achieved satisfying results

Controller Design for Fractional-Order Systems

In recent time, the application of fractional derivatives has become quite apparent in modeling mechanical and electrical properties of real materials. Fractional integrals and derivatives has found wide application in the control of dynamical systems, when the controlled system or/and the controller is described by a set of fractional order differential equations. In the present work a fractional order system has been represented by a higher integer order system, which is further approximated by second order plus time delay (SOPTD) model. The approximation to a SOPTD model is carried out by the minimization of the two norm of the actual and approximated system. Further, the effectiveness of a fractional order controller in meeting a set of frequency domain specifications is determined based on the frequency response of an integer order PID and a fractional order PID (FOPID) controller, designed for the approximated SOPTD model. The advent of fuzzy logic has led to greater flexibility in designing controllers for systems with time varying and nonlinear characteristics by exploiting the system observations in a linguistic manner. In this regard, a fractional order fuzzy PID controller has been developed based on the minimization different optimal control based integral performance indices. The indices have been minimized using genetic algorithms. Simulation results show that the fuzzy fractional order PID controller is able to outperform the classical PID, fuzzy PID and FOPID controllers

Modelado dinámico del sistema respiratorio ante incrementos de demanda ventilatoria, enfermedades pulmonares y ventilación mecánica asistida

Respiratory diseases remain one of the leading causes of death and illness in Europe and worldwide. One of the most important is Chronic Obstructive Pulmonary Disease (COPD) associated mainly with chronic bronchitis and pulmonary emphysema. Patients with COPD during acute respiratory failure (ARF) require mechanical ventilation to assist or replace their lung function, where the selection of ventilatory mode and its configuration is an essential step for patient's treatment and recovery.The development of pathophysiological knowledge and technology has generated a wide variety of ventilation modes designed to increase alveolar ventilation, reduce respiratory work, improve the coupling between ventilation and perfusion and optimize oxygenation of arterial blood. In clinical practice, however, many of the benefits they provide are often unused because of: 1) the complexity and diversity of ventilatory modalities and ventilator brands, and 2) the lack of tools to assist in the proper selection and configuration of the ventilatory modes according to the specific characteristics of each patient.Several models of the respiratory system have been studied to enhance knowledge about the mechanism of ventilatory control that the system adopts in normal and pathological conditions and to predict its cardiorespiratory response. However, the connection between the respiratory control system and mechanical ventilators remains an open research field, since it is essential to know and predict properly the respiratory pattern and the parameters that affect it before to set up the ventilator.The main objective of this doctoral thesis is the developed and evaluation of new computational simulators that allow predicting appropriately the respiratory dynamic response of healthy subjects and respiratory patients under ventilatory demands and assisted mechanical ventilation.In this thesis, different models of the respiratory system are analyzed. Modifications in their modeling, adjustments in their parameters and comparative studies were performed in order to properly predict the response of the respiratory system in healthy and pathological subjects during increased ventilatory demand. In addition, a computational and interactive tool, based on a model that integrates the most relevant characteristics of the analyzed models and a model of a mechanical ventilator, has been developed to simulate the interaction between a respiratory patient and a mechanical ventilator.The main contributions of the thesis are:1) A new estimate of respiratory mechanical work with a better physiological meaning and whose minimization allows better prediction of the system control response. 2) A complete respiratory system model that properly predicts both transient and stationary response of a healthy subject under incremental ventilatory demands. This model uses an improved gas exchange and sensing respiratory plant and more appropriate optimization algorithms.3) A complete model of the respiratory system that adequately predicts the response of obstructive and restrictive lung diseases. This model incorporates the simplification of a well-known, detailed and complete respiratory mechanical plant that is approximated quadratically for its computational integration in the model of the previous healthy subject. Mechanical parameters of three submodels for each disease are also proposed. 5) A computer simulator with a friendly and interactive user's interface, which includes the previous analyzed models and a mechanical ventilator model. This tool, which has already been tested for usability, has been successfully used in courses for physicians, researchers and students. With all these tools, it is expected to provide resources that assist physicians in the configuration of mechanical ventilators and understanding the interaction patient-ventilator.Las enfermedades respiratorias son una de las causas principales de muerte y enfermedad en Europa y el mundo. Una de las más importantes es la Enfermedad Pulmonar Obstructiva Crónica (EPOC) asociada principalmente a la bronquitis crónica y al enfisema pulmonar. Los pacientes con EPOC durante una Insuficiencia Respiratoria Aguda (IRA) requieren ventilación mecánica para asistir o sustituir su función pulmonar, donde la selección y configuración del modo ventilatorio constituye un paso esencial durante el tratamiento y la recuperación del paciente. La evolución del conocimiento fisiopatológico y de la tecnología ha generado una gran variedad de modos de ventilación diseñados para aumentar la ventilación alveolar, reducir el trabajo respiratorio, mejorar el acoplamiento entre la ventilación y la perfusión y optimar la oxigenación de la sangre arterial. Sin embargo, en la práctica clínica suelen ser desaprovechados muchos de los beneficios que estos ofrecen debido a: 1) la complejidad y diversidad de modos ventilatorios y marcas de ventiladores, y 2) la falta de herramientas que ayuden a la selección y configuración adecuada de estos en función de las características específicas de cada paciente. Se han estudiado diversos modelos del sistema respiratorio para reforzar el conocimiento sobre el mecanismo de control ventilatorio que dicho sistema adopta en condiciones normales y patológicas. Sin embargo, la unión entre el sistema de control respiratorio y los ventiladores mecánicos sigue siendo un campo de investigación abierto, dado que antes de configurar el ventilador resulta fundamental conocer y predecir apropiadamente su patrón respiratorio y los parámetros que lo afectan. El objetivo principal de esta tesis es el desarrollo y evaluación de nuevos simuladores computacionales que permitan predecir apropiadamente la respuesta dinámica respiratoria de sujetos sanos y enfermos respiratorios ante demandas ventilatorias y ventilación mecánica asistida. En esta tesis diversos modelos del sistema respiratorio son analizados. Modificaciones en su modelado, ajustes en sus parámetros y estudios comparativos fueron realizados con el fin de predecir adecuadamente la respuesta del sistema respiratorio en sujetos sanos y patológicos durante demandas ventilatorias incrementadas. Además, una herramienta computacional, basada en un modelo que integra las características más relevantes de los modelos analizados y de un ventilador mecánico, ha sido desarrollada para simular la interacción paciente-ventilador. Las principales contribuciones de la tesis son: 1) Una nueva estimación del trabajo mecánico respiratorio con una mayor interpretación fisiológica y cuya minimización permite predecir mejor la respuesta del sistema de control. 2) Un modelo completo del sistema respiratorio que predice adecuadamente la respuesta tanto en régimen transitorio como estacionario de un sujeto sano ante demandas ventilatorias incrementales. Dicho modelo utiliza una planta respiratoria de intercambio y sensado de gases mejorada y algoritmos de optimización más apropiados. 3) Un modelo completo del sistema respiratorio que predice adecuadamente la respuesta de enfermedades pulmonares obstructivas y restrictivas. Dicho modelo incorpora la simplificación de una planta mecánica respiratoria conocida, detallada y completa que se aproxima cuadráticamente para su integración computacional en el modelo del sujeto sano anterior. Parámetros mecánicos de tres submodelos para cada enfermedad son también propuestos 5) Un simulador computacional con una interfaz amigable e interactiva, que incluye el modelo anterior de un paciente y de un ventilador mecánico. Dicha herramienta a la que ya se le han hecho pruebas de usabilidad, ha sido utilizada con éxito en cursos para médicos, investigadores y estudiantes. (...)Postprint (published version