6 research outputs found

    Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

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    Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

    Implementaci贸n experimental de esquemas de control avanzado mediante t茅cnicas inteligentes sobre el reactor de fusi贸n de la UPV/EHU

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    En este Trabajo Fin de M谩ster se tiene por objetivo el dise帽o e implementaci贸n de sistemas de control inteligentes para el control de la corriente el茅ctrica del reactor de fusi贸n nuclear experimental de tipo Stellarator ULISES UPV/EHU. Para ello, se detalla el proceso llevado a cabo desde la captura de los datos para obtener el modelo equivalente del sistema constituido por el reactor y la fuente de alimentaci贸n DC, pasando por el estudio te贸rico de los distintos tipos de control inteligente propuestos y por 煤ltimo la implementaci贸n en tiempo real de los mismos. Asimismo, este proyecto supone un paso previo a futuras investigaciones entorno del ULISES UPV/EHU con el objetivo de mejorar la estabilidad del plasma generado en el interior del reactor.The objective of this Master Thesis is the design and implementation of intelligent control systems for the electric current control of the experimental stellarator nuclear fusion reactor ULISES UPV/EHU. For this purpose, the process carried out is detailed from the data capture to obtain the equivalent model of the system consisting of the reactor and the DC power supply, through the theoretical study of the different types of intelligent control proposed and finally their implementation in real time. Likewise, this project is a preliminary step for future research around ULISES UPV/EHU with the aim of improving the stability of the plasma generated inside the reactor.Master Amaierako Lan honen helburua, Stellarator motako fusio nuklearreko ULISES UPV/EHU erreaktoreko korronte elektrikoaren kontrola gauzatzeko kontrol sistema adimendunen garapen eta inplementazioa da. Horretarako, erreaktore eta DC elikadura iturriak osatutako sistemaren modeloa lortzeko beharrezko pausuetatik hasita, proposaturiko adimendun kontrol mota desberdinen ikasketa teorikotik pasatuz, beraien denbora errealeko inplementaziora egin beharreko pausuak azaltzen dira. Honez gain, proiektu honek, ULISES UPV/EHU erreaktorearen inguruan egingo diren ikerkuntzen aurrerapauso bezala balio izan du, erreaktorearen barnean eratzen den plasmaren egonkortasuna hobetzeko asmoa delarik azken helburua

    Desarrollo de una estrategia de control inteligente sobre una plataforma de simulaci贸n de un helic贸ptero (Twin Rotor)

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    Hoy en d铆a, con la continua aparici贸n de nuevas tecnolog铆as y estrategias de control, surge la problem谩tica de combinar ambas para el desarrollo de nuevos sistemas avanzados de control, tanto en procesos productivos como en aplicaciones de diferentes 谩reas. A medida que los dispositivos de control ganan en prestaciones, se ofrecen novedosas alternativas a las t茅cnicas existentes. Prueba de ello es que las t茅cnicas procedentes de la Computaci贸n Inteligente ganan terreno en el dise帽o de controladores m谩s robustos y eficaces, en el an谩lisis de informaci贸n procedente de procesos o plantas, o incluso en los procesos de optimizaci贸n y tomas de decisi贸n. Por tanto, el principal objetivo del presente trabajo es avanzar en el estudio de una estrategia de control basada en t茅cnicas inteligentes para una plataforma Twin-Rotor, simulando el control de un helic贸ptero. Dicha estrategia ya ha sido contrastada en sistemas monovariables (SISO) y se propone dar el salto a un sistema multivariable (MIMO).Gaur egun sortzen ari diren teknologia berriak zein kontrol estrategiak direla eta, hauek konbinatzeko arazoak sortu dira kontrol sistema aurreratu berriak garatzerako orduan, produkzio prozesuetan baita arlo desberdinetako aplikazioetan ere. Kontrol gailuek errendimendua hobetzen duten heinean, teknika berriak eskaintzen dira. Hori egiaztatu da, Konputazio Adimenean parte hartzen duten teknikek indarra hartzen ari direlako, kontrolagailu sendoagoak eta eraginkorragoak diseinatzerakoan, lantegietako informazioa aztertzerakoan zein optimizazio edo erabakiak hartzeko prozesuetan. Ondorioz, lan honen helburu nagusia Twin-Rotor izeneko plataforma baterako adimen teknika ezberdinetan oinarritutako estrategiak ikertzea da, helikoptero baten kontrola simulatuz. Estrategia hau dagoeneko kontrastatu da sistema mono-aldagaietan (SISO). Azkenik, lan honekin lortu nahi dena sistema multi-aldagaietan (MIMO) erabiltzea da.Nowadays, due to the continuous appearance of new technologies and control strategies, the problem of combining both for the development of new advanced control systems appears, in production processes and in applications in different areas. As control devices gain in performance, novel alternatives to existing techniques are offered. Proof of this is that the techniques from Intelligent Computing are gaining ground in the design of more robust and efficient controllers, in the analysis of information from processes or plants, or even in the optimization and decision-making processes. Therefore, the main objective of this work is to advance in the study of a control strategy based on intelligent techniques for a Twin-Rotor platform, simulating the control of a helicopter. This strategy has already been tested in monovariable systems (SISO) and it is proposed to make the leap to a multivariable system (MIMO)

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

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    Los cap铆tulos 2,3 y 7 est谩n sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

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    Los cap铆tulos 2,3 y 7 est谩n sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    Desarrollo y validaci贸n de una metodolog铆a para la implementaci贸n industrial de un control predictivo basado en modelo en un controlador de automatizaci贸n programable

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    El objetivo principal de la tesis fue desarrollar y validar una metodolog铆a de control predictivo basado en modelo implementado en un controlador de automatizaci贸n programable. Para cumplir con ese objetivo se da inicio con la historia y los aspectos generales que describen el control predictivo basado en modelo. Dentro de sus diferentes tipos de controladores, encontramos uno de los pioneros que es el Control por matriz din谩mica. Este controlador se describe por tres elementos: el modelo de predicci贸n, la forma en que rechaza perturbaciones y el algoritmo de control. Una vez se han explicado de manera detallada esos aspectos, se incluyen dos caracter铆sticas muy importantes. La primera corresponde a la fundamentaci贸n matem谩tica que permite controlar sistemas multivariables. La segunda, a la inclusi贸n de restricciones en las se帽ales de salida y de control. Posteriormente, se desarrolla el algoritmo de Control por matriz din谩mica y se v谩lida en el software Matlab. Se inicia con un an谩lisis del control predictivo basado en modelo. Luego, se valida el c贸digo para sistemas SISO y MIMO, obteniendo la respuesta esperada al implementar la t茅cnica de control. Continuamos, con la descripci贸n de la columna de destilaci贸n. Partiendo de unas suposiciones y consideraciones se obtienen las ecuaciones que describen el comportamiento din谩mico del proceso. Se linealiza el modelo para obtener las funciones de transferencia que alimentar谩n la din谩mica del controlador predictivo. Por 煤ltimo, se implementa el controlador en un controlador de automatizaci贸n programable validando el desarrollo con dos ejemplos. Analizando el comportamiento de los sistemas previamente validados en Matlab.Abstract: The main objective of the thesis is to develop and validate a model predictive control implemented in a programmable automation controller. In order to achieve this goal, we begin with the history and general aspects that describe model predictive control. Within its different types of controllers, we find one of the pioneers that is Dynamic matrix control. This controller is described by three elements which are as follows: the prediction model, measurable disturbances and the control algorithm. Once these aspects have been explained in detail, two very important characteristics are included. The first corresponds to the mathematical explanation that allows controlling multivariate systems. The second is the inclusion of restrictions on the output and control signals. Subsequently, the Dynamic Matrix Control algorithm is developed and validated in Matlab software. It starts with an analysis of the model predictive control. Then, the algorithm is validated for SISO and MIMO systems, obtaining the expected response when a control technique is implemented. We continue with the description of the distillation column, starting with some assumptions and considerations, we obtain the equations that describe the dynamic behavior of the process. The model is linearized to obtain the transfer functions that will feed the dynamics of the predictive controller. Finally, the controller is implemented in a programmable automation controller validating the algorithm with two systems. Analyzing the behavior of the system that were previously validated in Matlab.Maestr铆
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