Neurofuzzy model of an industrial process, reducing complexity by using principal component analysis

XVI Congreso Español sobre Tecnologías y Lógica Fuzzy Valladolid, 1-3 de febrero de 2012A Neurofuzzy model of a mixing chamber pressure has been proposed. The process is a part of a copper smelter plant. The principal component analysis (PCA) method has been used to reduce the inputs space for a recurrent fuzzy model. The coupling among variables and their mutual influence between themselves, are taken into account by the projection into the PCA axis. The model have been validated with real data from the factory. The validation result shows that the model is suitable for simulation.Ministerio de Ciencia e Innovación DPI2010-21589-C05-0

Modelo neuroborroso de la presión de la cámara de mezcla en una fundición de cobre

JORNADAS DE AUTOMÁTICA (27) (27.2006.ALMERÍA)En este artículo se presenta un modelo neuroborroso de la presión de una cámara de mezclas de gases en una fundición de cobre, dentro del circuito de tratamiento de gases de la fábrica. Se ha usado el método de análisis de componentes principales para la reducción del espacio de variables de entrada y se ha obtenido además un modelo no recurrente, usándose para ello modelos neuroborrosos jerárquicos. El modelo se ha validado con datos experimentales obtenidos en una planta real.Ministerio de Educación y Ciencia DPI2004-07444-C04-0

Fuzzy model predictive control. Complexity reduction by functional principal component analysis

En el Control Predictivo basado en Modelo, el controlador ejecuta una optimización en tiempo real para obtener la mejor solución para la acción de control. Un problema de optimización se resuelve para identificar la mejor acción de control que minimiza una función de coste relacionada con las predicciones de proceso. Debido a la carga computacional de los algoritmos, el control predictivo sujeto a restricciones, no es adecuado para funcionar en cualquier plataforma de hardware. Las técnicas de control predictivo son bien conocidos en la industria de proceso durante décadas. Es cada vez más atractiva la aplicación de técnicas de control avanzadas basadas en modelos a otros muchos campos tales como la automatización de edificios, los teléfonos inteligentes, redes de sensores inalámbricos, etc., donde las plataformas de hardware nunca se han conocido por tener una elevada potencia de cálculo. El objetivo principal de esta tesis es establecer una metodología para reducir la complejidad de cálculo al aplicar control predictivo basado en modelos no lineales sujetos a restricciones, utilizando como plataforma, sistemas de hardware de baja potencia de cálculo, permitiendo una implementación basado en estándares de la industria. La metodología se basa en la aplicación del análisis de componentes principales funcionales, proporcionando un enfoque matemáticamente elegante para reducir la complejidad de los sistemas basados en reglas, como los sistemas borrosos y los sistemas lineales a trozos. Lo que permite reducir la carga computacional en el control predictivo basado en modelos, sujetos o no a restricciones. La idea de utilizar sistemas de inferencia borrosos, además de permitir el modelado de sistemas no lineales o complejos, dota de una estructura formal que permite la implementación de la técnica de reducción de la complejidad mencionada anteriormente. En esta tesis, además de las contribuciones teóricas, se describe el trabajo realizado con plantas reales en los que se han llevado a cabo tareas de modelado y control borroso. Uno de los objetivos a cubrir en el período de la investigación y el desarrollo de la tesis ha sido la experimentación con sistemas borrosos, su simplificación y aplicación a sistemas industriales. La tesis proporciona un marco de conocimiento práctico, basado en la experiencia.In Model-based Predictive Control, the controller runs a real-time optimisation to obtain the best solution for the control action. An optimisation problem is solved to identify the best control action that minimises a cost function related to the process predictions. Due to the computational load of the algorithms, predictive control subject to restric- tions is not suitable to run on any hardware platform. Predictive control techniques have been well known in the process industry for decades. The application of advanced control techniques based on models is becoming increasingly attractive in other fields such as building automation, smart phones, wireless sensor networks, etc., as the hardware platforms have never been known to have high computing power. The main purpose of this thesis is to establish a methodology to reduce the computational complexity of applying nonlinear model based predictive control systems subject to constraints, using as a platform hardware systems with low computational power, allowing a realistic implementation based on industry standards. The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexity of rule-based systems, like fuzzy and piece wise affine systems, allowing the reduction of the computational load on modelbased predictive control systems, subject or not subject to constraints. The idea of using fuzzy inference systems, in addition to allowing nonlinear or complex systems modelling, endows a formal structure which enables implementation of the aforementioned complexity reduction technique. This thesis, in addition to theoretical contributions, describes the work done with real plants on which tasks of modeling and fuzzy control have been carried out. One of the objectives to be covered for the period of research and development of the thesis has been training with fuzzy systems and their simplification and application to industrial systems. The thesis provides a practical knowledge framework, based on experience

Development and Experimental Validation of a Dynamic Model for a Fresnel Solar Collector

this paper presents a lumped parameter dynamic model of a Fresnel collector field of a solar refrigeration plant. The plant is located in the Escuela Superior de Ingenieros of the University of Seville. The dynamic model parameter model developed can be used as a control model or as a simulation tool to test controllers. The lumped parameters have been determined by using real data from the plant in different operating conditions. The model has been validated against a data validation set obtained from the plant. The model has shown to reproduce the system behavior with a good compromise in accuracy and model complexity

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Fuzzy Model Predictive Control: Complexity Reduction for Implementation in Industrial Systems

In this paper, a new fuzzy logic-based control-design technique is presented. The method aims at reducing the complexity of Takagi-Sugeno Fuzzy systems via the reduction of fuzzy rules. This reduction is obtained by finding a function basis via the Functional Principal Component Analysis, and then the model is used for Model Predictive Control (MPC). This procedure is systematic, and eventually leads to feasible low-cost microcontroller-based implementations, which has become a generic need in the era of IoT. In order to validate the results, two experimental setups have been controlled using these principles. The first of these, a mechanical pendulum, presents nonlinear dynamics that suggests the use of linear discrete models at specific operating points. In the second, a pilot plant implementing an industrial process with a chemical reactor and a heat exchanger, presents nonlinear multivariate dynamics that are successfully handled with the Fuzzy MPC Controller

Simplified Fuzzy Model Based Predictive Control for a Nonlinear System

[Abstract] A reduced complexity fuzzy model has been developed to capture the nonlinear dynamics of a mechanical system. The use of Functional Principal Analysis to reduce the complexity of the model permitted the use of a linear controller based on that modelThe authors gratefully acknowledge the Spanish Ministry of Economy and Competitivenes for its financial support of part of this work through the grant DPI2013-46912-C2-1https://doi.org/10.17979/spudc.978849749808

Hybrid Nonlinear MPC of a Solar Cooling Plant

Solar energy for cooling systems has been widely used to fulfill the growing air conditioning demand. The advantage of this approach is based on the fact that the need of air conditioning is usually well correlated to solar radiation. These kinds of plants can work in different operation modes resulting on a hybrid system. The control approaches designed for this kind of plant have usually a twofold goal: (a) regulating the outlet temperature of the solar collector field and (b) choosing the operation mode. Since the operation mode is defined by a set of valve positions (discrete variables), the overall control problem is a nonlinear optimization problem which involves discrete and continuous variables. This problems are difficult to solve within the normal sampling times for control purposes (around 20–30 s). In this paper, a two layer control strategy is proposed. The first layer is a nonlinear model predictive controller for regulating the outlet temperature of the solar field. The second layer is a fuzzy algorithm which selects the adequate operation mode for the plant taken into account the operation conditions. The control strategy is tested on a model of the plant showing a proper performance.Unión Europea OCONTSOLAR ID 78905

Modelo en Ecosimpro® de captador solar Fresnel

XXXIII Jornadas de Automática. 05/09/2012. VigoSe ha desarrollado en este trabajo un conjunto de componentes de EcosimPro® para la simulación del captador tipo Fresnel de una planta solar situada en la Escuela Técnica Superior de Ingeniería de la Universidad de Sevilla. Se ha basado en un modelo de parámetros distribuidos, ajustando los parámetros del mismo con datos tomados del sistema real y comparando la respuesta del modelo con la temperatura de salida real del sistema.Ministerio de Eduación y Ciencia DPI2010-21589- C05-0

Modelado y control de sistemas industriales para la extracción de aceite

JORNADAS DE AUTOMÁTICA (32) (32.2011.SEVILLA, ESPAÑA)En este art´ıculo se aborda el modelado y simulaci´on de una almazara de extracci´on de aceite. El modelo es obtenido empleando ecuaciones de primeros principios cuyos par´ametros son ajustados con datos reales obtenidos de una almazara situada en Malag´on (Ciudad Real). Estos modelos, construidos tanto en Ecosim como en Matlab sirven a su vez de soporte para poder realizar un an´alisis de la din´amica de la planta y proponer una estrategia de control que mejore el rendimiento global del proceso