Análisis y diseño de sistemas de control de procesos basados en lógica borrosa

E1 objetivo de esta Tesis es profundizar en el análisis de los Sistemas de Control Borroso, con el fin de desarrollar una metodología que facilite el diseño de este tipo de sistemas. La razón de emplear la lógica borrosa como técnica reside en que la mayoría de los problemas reales que aparecen en el control de procesos industriales tienen un alto contenido en vaguedad e incertidumbre. La lógica borrosa permite abordar problemas definidos en términos lingüísticos, y por tanto imprecisos, donde los datos están expresados en términos cualitativos. De esta forma, es posible plantear el problema en los mismos términos en los que lo haría un experto humano. Si bien existen numerosas aplicaciones en control de procesos de la lógica borrosa, no existe una metodología que permita independientemente de las características del proceso, configurar el sistema de control. Hasta el momento los resultados más satisfactorios se han obtenido de manera empírica. El desarrollo de este trabajo pretende ofrecer una solución metódica al diseño de sistemas de control borroso. Se abarcan casi todos los aspectos relativos al diseño, desde la construcción de la tabla de reglas, su ajuste y posterior calibración, hasta temas de modelado, análisis dinámico y supervisión. Por último, se presenta una herramienta de desarrollo que permite construir cómodamente módulos de control borroso. ABSTRACT i he main goal of this Thesis is to research on the analysis of Fuzzy Control Systems in order to develop a design methodology. The use of fuizzy logic as technique is justified because the most actual problems in industrial process control have a lot content in vagueness and uncertainty. Fuzzy logic allows to cope with problems defmed by using linguistic statements, where data are expressed in qualitative terms. By the way, it is possible to formúlate the problem in the same way as the human expert does. Although there exist a lot of fuzzy logic applications in process control, it does not exist a methodology to configure the control system independently from the process behavior. At present the most successful results have been reached empirically. This work tries to present a methodic solution to fuzzy control systems design. Almost all topics related with design are conunented, the building of the table of rules, it adjust and later calibration, modelling, dynamic analysis and supervisión. Finally, we present a development tool which allows to build easily fuzzy control modules

Distributed orientation agreement in a group of robots

In this article, a method for the agreement of a set of robots on a common reference orientation based on a distributed consensus algorithm is described. It only needs that robots detect the relative positions of their neighbors and communicate with them. Two different consensus algorithms based on the exchange of information are proposed, tested and analyzed. Systematic experiments were carried out in simulation and with real robots in order to test the method. Experimental results show that the robots are able to agree on the reference orientation under certain conditions. Scalability with an increasing number of robots was tested successfully in simulation with up to 49 robots. Experiments with real robots succeeded proving that the proposed method works in reality

An introduction to swarm robotics

Swarm robotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and flexibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

New Optimal Approach for the Identification of Takagi-Sugeno Fuzzy Model

A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (TS) fuzzy model. The idea comes from the fact that the main drawback of T-S model is that it can not be applied when the membership functions are overlapped by pairs. This limits the application of the T-S model because this type of membership function has been widely used in the stability and controller design of fuzzy systems. It is also very popular in industrial control applications. The method presented here can be considered as a generalized version of T-S fuzzy model with optimized performance in approximating nonlinear functions. Various examples are chosen to show the high function approximation accuracy and fast convergence obtained by applying the proposed method in approximating nonlinear systems locally and globally in comparison with the original T-S model

Estimación de modelos borrosos y su aplicación al control óptimo

El objetivo de la presentación es dar a conocer los últimos trabajos del Grupo de Investigación sobre últimos trabajos del Grupo de Investigación sobre Control Borroso. • Obtención de modelos precisos de sistemas no lineales basados en sistemas borrosos – Mamdani – Takagi-Sugeno – Linealización • Generalización del método propuesto por T-S • Identificación iterativa basada en el Filtro de Kalman • Sistemas de control basados en el modelo TS obtenido – LQ

Control en cascada clásico y borroso para el seguimiento de trayectorias. Apuntes para un estudio.

Los nuevos procesos de microfabricación imponen nuevos requisitos de precisión y robustez en los sistemas de control de posición y trayectoria, lo que abre nuevas líneas de investigación en el campo del modelado y el control, y la necesidad de evaluar técnicas de control inteligente tales como el control borroso. En este trabajo, se presenta por una parte el modelado clásico de partes eléctricas y mecánicas consideradas como un sistema de múltiples masas acopladas mediante una transmisión elástica y amortiguamiento, en presencia de la fricción y la holgura, dos no linealidades duras. Además, se muestra el diseño de un controlador a partir de un modelo paramétrico dependiente de la frecuencia de resonancia y del amortiguamiento. Como paso inicial del estudio, se diseña un sistema de control en cascada dotado de componentes anticipativas que es el esquema más utilizado en la industria. Con vistas a evaluar el alcance de las no linealidades en la ley de control, se sintetiza un control borroso en cascada equivalente a partir del método propuesto por Matia et al. 1992. Para evaluar el comportamiento del sistema de control, se consideraron incertidumbres en parámetros tales como la frecuencia de resonancia, el amortiguamiento y el ancho de la zona muerta de la holgura y se realizaron simulaciones considerando trayectorias circulares. Algunas cifras de mérito tales como la integral del valor absoluto del error en el tiempo (ITAE), el error máximo absoluto (MAE) y la integral del valor absoluto de la señal de control (IAU) se utilizaron en el estudio comparativo de ambos controladores en cascada. El estudio permitió comprobar que no hay diferencias significativas en el comportamiento de ambos sistemas de control (cascada clásico y cascada borroso)

An Optimal T-S Model for the Estimation and Identification of Nonlinear Functions

A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (TS) fuzzy model. The idea comes from the fact that the main drawback of T-S model is that it can not be applied when the membership functions are overlapped by pairs. This limits the application of the T-S model because this type of membership function has been widely used in the stability and controller design of fuzzy systems. It is also very popular in industrial control applications. The method presented here can be considered as a generalized version of T-S fuzzy model with optimized performance in approximating nonlinear functions. Various examples are chosen to show the high function approximation accuracy and fast convergence obtained by applying the proposed method in approximating nonlinear systems locally and globally in comparison with the original T-S model

A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems

An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters' weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm

An Approach to Flocking of Robots Using Minimal Local Sensing and Common Orientation

A new algorithm for the control of robot flocking is presented. Flocks of mobile robots are created by the use of local control rules in a fully distributed way, using just local information from simple infra-red sensors and global heading information on each robot. Experiments were done to test the algorithm, yielding results in which robots behaved as expected, moving at a reasonable velocity and in a cohesive way. Up to seven robots were used in real experiments and up to fifty in simulation