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Vick, Andrew W. "Genetic Fuzzy Controller for a Gas Turbine Fuel System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1291053513.
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Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.
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This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.
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麥禮安 and Lai-on Mak. "Fuzzy logic statcom controller design with genetic algorithm application for stability enhancement of interconnected power systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B42128699.
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Mak, Lai-on. "Fuzzy logic statcom controller design with genetic algorithm application for stability enhancement of interconnected power systems." Click to view the E-thesis via HKUTO, 2000. http://sunzi.lib.hku.hk/hkuto/record/B42128699.
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Lima, Robson Pacífico Guimarães. "Uma aplicação baseada em sistemas imunológicos artificiais para detecção de falhas em uma plataforma de abastecimento." Universidade Federal da Paraíba, 2013. http://tede.biblioteca.ufpb.br:8080/handle/tede/5293.
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Previous issue date: 2013-08-30Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In this work, an Artificial-Immune-System based anomaly detection system applied to Water Supply System is presented. At normal working, the pressure level into the system is controlled by a Fuzzy Control System. As the Water Supply System is composed of pressure sensors, valves, pumps, and other devices, faults in these devices causing abnormal disturbances can occur. An algorithm of Artificial-Immune-System, namely, the Negative Selection Algorithm, is the base of the proposed anomaly detection system. The Negative Selection Algorithm verifies abnormal system conditions based on the normal system conditions. Experimental results show that the proposed system is effective in order to detect anomaly.
Neste trabalho é apresentado um método de detecção automática de falhas, baseado em Sistemas Imunológicos Artificias, aplicado em um sistema de abastecimento de água. Este processo utiliza um Sistema de Controle Fuzzy para manter o nível de pressão estabilizado em seu princípio de operação normal do sistema. Esta plataforma de abastecimento de água é composta por sensores de pressão, válvulas, bombas e outros dispositivos. Falhas nos componentes que compõem a plataforma poderão ocorrer causando perturbações em seu funcionamento. Um algoritmo, extraído dos Sistemas Imunológicos Artificiais, denominado de Algoritmo de Seleção Negativa, é a base de detecção de falhas proposto neste trabalho. Este algoritmo verifica condições de operação anormais baseado nas condições de funcionamento normal do sistema. Resultados das simulações e experimentos acerca da utilização deste algoritmo foram obtidos comprovando a eficiência dessa técnica.
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Hitchings, Mark R., and n/a. "Distance and Tracking Control for Autonomous Vehicles." Griffith University. School of Microelectronic Engineering, 1999. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050902.084155.
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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.
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Hitchings, Mark. "Distance and Tracking Control for Autonomous Vehicles." Thesis, Griffith University, 1999. http://hdl.handle.net/10072/366396.
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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.Thesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Science, Environment, Engineering and Technology
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LIMA, Fernanda Maria Maciel de. "PROPOSTA DE CONTROLE NEBULOSO BASEADO EM CRITÉRIO DE ESTABILIDADE ROBUSTA NO DOMÍNIO DO TEMPO CONTÍNUO VIA ALGORITMO GENÉTICO MULTIOBJETIVO." Universidade Federal do Maranhão, 2015. http://tedebc.ufma.br:8080/jspui/handle/tede/1861.
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A fuzzy project Takagi-Sugeno (TS) with robust stability based on the specifications of the gain and phase margins via multi-objective genetic algorithm in continuos time domain is proposed in this master thesis. A Fuzzy C-means (FCM) clustering algorithm is used to estimate the antecedent parameters and rules number of a fuzzy TS model by means of the input and output experimental data of the plant to be controlled, while minimum squares algorithm estimate the consequent parameters. A multi-objective genetic strategy is defined to adjust the parameters of a fuzzy PID controller, so that, the gain and phase margins of the fuzzy control system are close to the specified values. Two theorems are proposed to analyse the necessary and sufficient conditions for the fuzzy PID controller design to ensure the robust stability in the close-loop control. The fuzzy PID controller was simulated in the Simulink environment and compared with lead and delay compensator. Experimental results obtained in a control platform in real time to validation the methodology proposed are presented and compared with fuzzy PID controller obtained by the Ziegler Nichols method. The results demonstrate the effectiveness and practical feasibility of the proposed methodology.
Um projeto de controle nebuloso Takagi-Sugeno(TS) com estabilidade robusta baseado nas especificações das margens de ganho e fase via algoritmo genético multiobjetivo no domínio do tempo contínuo é proposto nesta dissertação. Um algoritmo de agrupamento Fuzzy C-Means (FCM) é usado para estimar os parâmetros do antecedente e o número da regras de um modelo nebuloso TS, por meio dos dados experimentais de entrada e de saída da planta a ser controlada, enquanto que o algoritmo de mínimos quadrados estima os parâmetros do consequente. Uma estratégia genética multiobjetiva é definida para ajustar os parâmetros de um controlador PID nebuloso, de modo que, as margens de ganho e fase do sistema de controle nebuloso estejam próximos dos valores especificados. São propostos dois teoremas que analisam as condições necessárias e suficientes para o projeto do controlador PID nebuloso de modo a garantir a estabilidade robusta na malha de controle. O controlador PID nebuloso foi simulado no ambiente Simulink e comparado com compensadores de avanço e de atraso e os resultados analisados. Resultados experimentais obtidos em uma plataforma de controle, em tempo real, para validação da metodologia proposta são apresentados e comparado com controlador PID nebuloso obtido pelo método de Ziegler Nichols. Os resultados obtidos demonstram a eficácia e viabilidade prática da metodologia proposta.
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Carlos, Luiz Amorim. "Algoritmos gen?ticos: uso de l?gica nebulosa e an?lise de converg?ncia por cadeia de Markov." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15236.
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Previous issue date: 2013-11-05In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables
Neste trabalho, a cadeia de Markov ser? a ferramenta usada na modelagem e na an?lise de converg?ncia do algoritmo gen?tico, tanto para sua vers?o padr?o quanto para as demais vers?es que o algoritmo gen?tico permite. Al?m disso, pretende-se comparar o desempenho da vers?o padr?o com a vers?o nebulosa, por acreditar que esta vers?o d? ao algoritmo gen?tico uma grande capacidade para encontrar um ?timo global, pr?prio dos algoritmos de otimiza??o global. A escolha deste algoritmo deve-se tamb?m ao fato do mesmo ter se tornado, nos ?ltimos anos, uma das ferramentas mais usadas para achar uma solu??o do problema de otimiza??o. Esta escolha deve-se ? sua comprovada efic?cia na busca de uma solu??o de boa qualidade para o problema, considerando que o conhecimento de uma solu??o de boa qualidade torna-se aceit?vel tendo em vista que pode n?o existir um outro algorimo capaz de obter a solu??o ?tima, para muitos desses problemas. Entretanto, esse algoritmo pode ser definido, levando em conta que o mesmo ? dependente n?o apenas da forma como o problema ? representado, mas tamb?m como s?o definidos alguns dos operadores, desde sua vers?o padr?o, quando os par?metros s?o mantidos fixos, at? suas vers?es com par?metros vari?veis. Por isso, para se alcan?ar um bom desempenho com o aludido algoritmo ? necess?rio que o mesmo tenha um adequado crit?rio na escolha de seus par?metros, principalmente da taxa de muta??o e da taxa de cruzamento ou, at? mesmo, do tamanho da popula??o. ? importante lembrar que as implementa??es em que par?metros s?o mantidos fixos durante toda a execu??o, a modelagem do algoritmo por cadeia de Markov resulta numa cadeia homog?nea, e quando permite a varia??o de par?metros ao longo da execu??o, a cadeia de Markov que o modela passa a ser do tipo n?o-homog?nea. Portanto, na tentativa de melhorar o desempenho do algoritmo, alguns trabalhos t?m procurado realizar o ajuste dos par?metros atrav?s de estrat?gias que captem caracter?sticas intr?nsecas ao problema. Essas caracter?sticas s?o extra?das do estado presente de execu??o, com o fim de identificar e preservar algum padr?o relacionado a uma solu??o de boa qualidade e, ao mesmo tempo, descartando aquele padr?o de baixa qualidade. As estrat?gias de extra??o das caracter?sticas tanto podem usar t?cnicas precisas quanto t?cnicas nebulosas, sendo neste ?ltimo caso feita atrav?s de um controlador nebuloso. Com o fim de avaliar empiriccamente o desempenho de um algoritmo n?o-homog?neo, apresenta-se testes onde se compara o algoritmo gen?tico padr?o com o algoritmo gen?tico nebuloso, sendo a taxa de muta??o ajustada por um controlador nebuloso. Para isso, escolhe-se problemas de otimiza??o cujo n?mero de solu??es varia exponencialmente com o n?mero de vari?veis
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Pires, Danúbia Soares. "PROPOSTA DE CONTROLE NEBULOSO BASEADO EM CRITÉRIO DE ESTABILIDADE ROBUSTA NO DOMÍNIO DO TEMPO DISCRETO VIA ALGORITMO GENÉTICO MULTIOBJETIVO." Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/505.
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Previous issue date: 2013-09-30In this master thesis, a robust fuzzy digital PID control methodology based on gain and phase margins specifications, is proposed. A mathematical formulation, based on gain and phase margins specifications, the Takagi-Sugeno fuzzy model of the plant to be controlled, the structure of the digital PID controller and the time delay uncertain system, was developed. From input and output data of the plant, the fuzzy clustering Fuzzy C-Means (FCM) algorithm estimates the antecedent parameters (operation areas ) and the rules number of Takagi-Sugeno fuzzy model. The least squares algorithm provides the consequent parameters linear submodels. A multiobjective genetic strategy is defined to tune the fuzzy digital PID controller parameters, so the gain and phase margins specified to the fuzzy control system are get. An analysis of necessary and sufficient conditions for fuzzy digital PID controller design with robust stability, with the proposal of the two theorems are presented. The digital fuzzy PID controller was implemented on a platform designed for monitoring and control in real time, based on CompactRIO and LabVIEW 9073, National Instruments, of the Laboratory of Computational Intelligence Applied to Technology (ICAT/DEE/IFMA), applying the temperature control of a thermal plant. Experimental results show the efficiency of the proposed methodology, through tracking of the reference and the gain and phase margins keeping closed of the specified ones.
Nesta dissertação é proposta uma metodologia para projeto de controle PID digital nebuloso robusto baseado nas especificações das margens de ganho e fase. É desenvolvida uma formulação matemática, baseada nas especificações das margens de ganho e fase, no modelo nebuloso Takagi-Sugeno da planta a ser controlada, na estrutura do controlador PID digital e o atraso de tempo do sistema incerto. A partir dos dados de entrada e saída da planta, o algoritmo de agrupamento nebuloso Fuzzy C-Means (FCM), estima os parâmetros do antecedente (regiões de operação) e o número de regras do modelo nebuloso Takagi-Sugeno. O algoritmo de mínimos quadrados fornece os parâmetros dos submodelos lineares do consequente. Uma estratégia genética multiobjetiva é utilizada para encontrar os parâmetros do controlador PID digital nebuloso, de modo que as margens de ganho e fase especificadas para o sistema de controle nebuloso sejam alcançadas. Uma análise das condições necessárias e suficientes para o projeto do controlador PID digital nebuloso com estabilidade robusta, a partir da proposta de dois teoremas, é apresentada. O controlador PID digital nebuloso projetado foi implementado numa plataforma para supervisão e controle em tempo real, baseada no CompactRIO 9073 e no software LabVIEW, da National Instruments, do Laboratório de Inteligência Computacional Aplicada à Tecnologia (ICAT/DEE/IFMA), com aplicação ao controle de temperatura de uma planta térmica. Resultados experimentais mostram a eficiência da metodologia proposta, uma vez que a trajetória de referência é seguida e as margens de ganho e fase permanecem próximas às especificadas.
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Yamazaki, Tsukasa. "An improved algorithm for a self-organising controllerd its experimental analysis." Thesis, Queen Mary, University of London, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320959.
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McClintock, Shaunna. "Soft computing : a fuzzy logic controlled genetic algorithm environment." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268579.
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Karaboga, Dervis. "Design of fuzzy logic controllers using genetic algorithms." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296639.
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鄺世凌 and Sai-ling Kwong. "Evolutionary design of fuzzy-logic controllers for manufacturing systems with production time-delays." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B3124323X.
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唐靜敏 and Ching-mun Tong. "Evolutionary design of fuzzy-logic controllers with minimal rule sets for manufacturing systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243678.
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Tong, Ching-mun. "Evolutionary design of fuzzy-logic controllers with minimal rule sets for manufacturing systems /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25100130.
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Kwong, Sai-ling. "Evolutionary design of fuzzy-logic controllers for manufacturing systems with production time-delays /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25100178.
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Dias, Rafael Nunes Hidalgo Monteiro. "Análise comparativa de técnicas de controle Fuzzy e matriz dinâmica aplicadas à máquina de corrente contínua." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/8098.
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This work presents a comparison between Fuzzy and dynamic matrix controllers. These controllers are applied to the direct current (DC) motor speed control, triggered by fully controlled three-phase rectifier. The construction of the real system and the development and validation of the computational model are described. The controllers’ parameters are obtained through an optimization process. Both control techniques are compared and results indicate better performance of the optimized controllers, which suggest their promise in nonlinear systems’ control, in which seeks out control without error, that fulfills well its duty and its able to resist the fatigues.
Este trabalho apresenta o comparativo entre os controladores Fuzzy e matriz dinâmica. Estes controladores são aplicados ao controle de velocidade do motor de corrente contínua, acionado por retificador trifásico totalmente controlado. A metodologia parte da construção do sistema real e do desenvolvimento e validação do modelo computacional. A obtenção dos parâmetros dos controladores é realizada através do processo de otimização. Realiza-se análise comparativa entre as técnicas de controle e os resultados apontam para a proeminência de controladores sintonizados via processo de otimização como técnica promissora a ser empregada em controle de sistemas não lineares, nos quais buscam-se controle em que não há erro, que cumpra bem o seu dever e apto para resistir às fadigas.
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ANDRADE, José Flávio Barbosa de. "Sintonia de controlador fuzzy por algoritmo genético em sistema de nível de líquidos." Universidade Federal do Pará, 2014. http://repositorio.ufpa.br/jspui/handle/2011/5604.
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O presente trabalho demonstra a aplicação de um Algoritmo Genético com o intuito de projetar um controlador Fuzzy MISO, através da sintonia de seus parâmetros, em um processo experimental de nivelamento de líquido em um tanque, cuja dinâmica apresenta características não-lineares. Para o projeto e sintonia do controlador, foi utilizado o suporte do software Matlab, e seus pacotes Simulink e Global Optimization Toolbox. O Controlador Fuzzy ora projetado teve seu desempenho avaliado através de ensaios em tempo real em um Sistema de Nível de Liquido.
This work presents the application of a Genetic Algorithm in order to design a MISO Fuzzy Controller by tuning its parameters in an experimental process of leveling liquid in a tank whose dynamics has nonlinear characteristics. In the Fuzzy Controller design and tuning it was used the software Matlab and their packages Simulink and Global Optimization Toolbox. The Fuzzy Controller designed had its performance evaluated by a real time test in a Liquid Level System.
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Gruppioni, Édson Mulero. "Otimização de parâmetros de controladores difusos para estruturas inteligentes." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-01022016-155026/.
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As estruturas aeronáuticas estão sujeitas a diversas solicitações, devido principalmente às interações com o escoamento aerodinâmico, que podem causar distúrbios e vibrações, comprometendo seu desempenho. Diversas pesquisas vêm sendo realizadas para solucionar estes problemas. Dentre elas está o uso de atuadores e sensores piezelétricos integrados na estrutura, que juntamente com um sistema de controle passa a ser denominada estrutura inteligente, a qual promove o controle ativo de vibrações garantindo um aumento no desempenho. O objetivo deste trabalho é obter parâmetros ótimos de um controlador não convencional baseado na lógica difusa para controle de vibrações em uma viga com atuadores e sensores piezelétricos. A viga e elementos piezelétricos são modelados pelo método de elementos finitos utilizando o princípio variacional eletromecânico. O sistema de controle difuso, o qual está se tornando amplamente utilizado principalmente devido à sua capacidade de representar sistemas não lineares e complexos, é baseado nos modelos difusos de Mamdani e Takagi-Sugeno-Kang. A otimização é feita através de algoritmo genético que é um processo de procura probabilística baseado nas leis de seleção natural influenciadas pelas teorias de Charles Darwin. São otimizados os valores dos ganhos de controle, bem como os suportes dos conjuntos difusos da base de conhecimento. São feitas comparações com o controlador difuso obtido por processo de ajuste manual.Aeronautical structures are subject to a variety of loads, due mainly to the iteration with the aerodynamic flow that can present disturbances, compromising their performance. Various researches have been carried out to solve these problems. Among them, the use of piezoelectric actuators and sensors integrated to the structure, jointly with a control system, the so-called smart structure technology, has been seen with good potentiaI. A smart structure promotes active vibration control, guaranteeing a performance increase. The objective of this work is to obtain optimal control parameters of a non-conventional vibration controller based on the fuzzy logic. A smart beam with piezoelectric actuators and sensors, that has been modeled by the finite element method, has been used to controI. The fuzzy control, which is becoming broadly utilized, mainly due to its capacity to represent complex and non-linear systems, is based in Mamdani and Takagi-Sugeno-Kang fuzzy models. The optimization scheme is based on genetic algorithms, a methodology inspired on the natural selection laws influenced by the Darwin\'s theories. Gains values and membership functions are optimized. Comparisons with the fuzzy controller achieved by trial and error parameters tuning are presented.
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Su, Yong-Tian, and 蘇永田. "Fuzzy Genetic Algorithm Controller." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/15555172366609616091.
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碩士中華科技大學
電子工程研究所碩士班
101
Genetic algorithm is an optimization tool based on natural evolution. According to Darwin's theory of natural selection, species can adapt to the environment, the higher the chances of survival. The basic spirit is to follow the example of natural selection in biological community, survival of the fittest the natural laws of evolution. The genetic algorithm does not like some typical approach, It does not have a specific mathematical formula. This thesis uses general fuzzy controller as a basic framework, it joined the forced evolution method, hoping to enhance the effectiveness of genetic fuzzy controller and learning ability. This thesis uses Matlab software in simulation test, it can really achieve the expected results.
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Ferng, Ji-cherng, and 馮志誠. "Genetic Algorithm based Fuzzy PID controller Design." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/21733034936517366541.
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碩士國立臺北科技大學
機電整合研究所
87
The theme of this thesis is to propose a new method of designing a fuzzy PID controller named the hybrid self tuned fuzzy PID controller. Since the PD controller tends to increase the speed of system response while the PI controller tends to improve the steady state response, both fuzzy PI and PD controllers are integrated with respective scaling factor in the proposed fuzzy PID controller. The respective scaling factors of both the Fuzzy PI and PD controllers are adjusted by the fuzzy inference rules. Therefore, four sets of fuzzy rules are to be designed for the fuzzy PID controller including the rules for fuzzy PI controller, fuzzy PD controller, fuzzy scaling factor of fuzzy PI controller and fuzzy scaling factor of fuzzy PD controller, respectively. The Genetic Algorithm is applied to automatically design these fuzzy rules. In order to save the number of fuzzy rules to be learned, a genetic algorithm based single fuzzy rule learning method is proposed. The fuzzy rule is learned rule by rule based on the unit step response of the controlled system. It will be shown that the proposed fuzzy PID controller designed by the genetic algorithm based single fuzzy rule learning method greatly improves the transient response as well as the steady state response. It also will be shown that the proposed fuzzy PID controller outperforms the controllers proposed by the research in the field.
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Xu, Ming Tong, and 徐明同. "Fuzzy algorithm-scheduled servo controller for contouring." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/01862888797401311957.
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Nian, Zhn He, and 粘志河. "Optimal Fuzzy Controller Design By Genetic Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/73966082206548721514.
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碩士中州技術學院
工程技術研究所
96
The purpose of the study is about optimal fuzzy controller design by genetic algorithm. To consider all of the fuzzy control factors to code membership functions, fuzzy rules and scaling factors together, in order to preclude the limit of a single factor. Others also improve the genetic operators and mutation operator especially. To compare with the traditional genetic algorithm, proves the optimal genetic algorithm can avoid premature, and can’t fall in local search. To compare with the PID controller proves its response is fast, and overshoots is less. Finally the inverted pendulum system proves it can get the better control. Its response curve is smooth and steady. The nonlinear control system shows good dynamic performance, steady-state performance, disturbance rejection and robustness.
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Chang, Wei-En, and 張維恩. "Fuzzy model identification by FCM clustering algorithm and fuzzy controller design." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/37147018727277599666.
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碩士大同工學院
電機工程研究所
87
ABSTRACT Fuzzy controllers have been successfully applied in many cases to which conventional control algorithms are difficult to be applied. Recently, it was proven that fuzzy systems are capable of approximating any real continuous function to arbitrary accuracy. This result motivates us to use the fuzzy systems as identifiers for nonlinear dynamic systems, and then design the fuzzy controllers based on the fuzzy system. In the process of the identification, most of the techniques used in the literatures for fuzzy c-mean (FCM) clustering are based on off-line operation. Namely, these techniques require all the objects or the distance matrix to be available before the start of any FCM clustering routine and it seems impractical in some cases. In this thesis, we propose a modified FCM clustering algorithm named the recursive fuzzy c-mean (RFCM) clustering algorithm. Comparing with the traditional FCM, the proposed algorithm has the following advantages: (1) Lower memory size, and lower computational complexity. (2) It is more applicable than traditional FCM in system identification and fuzzy control. There are three main objectives in this thesis: (1) We derive the clustering algorithm to partition the universal data set. (2) The Takagi and Sugeno’s fuzzy models are used as identifier for nonlinear dynamic systems. (3) The fuzzy model-based controller design method for tracking control is proposed based on this fuzzy system. Simulation results show that the identification algorithm exhibits good performances and the fuzzy model-based controllers could perform successful tracking ability.
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Tian, Yun Xiang, and 田雲翔. "A fuzzy logic controller design using genetic algorithm." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/91415832927118411648.
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Wang, Ying-chung, and 王盈中. "new algorithm for learning of fuzzy controller from training." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/76999421287262314310.
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碩士華梵大學
機電工程研究所
86
In recent years, fuzzy rule-based systems have been widely and successfully applied to many control domains [2, 3, 4]. From a theoretical point of view, fuzzy logic systems were proven as universal approximators of any nonlinear continuous function on a compact set to arbitrary accuracy [11, 17, 20]. Basically, a set of linguistic control rules is the kernel of a fuzzy inference system, which provides a approximate reasoning algorithm to convert expertise-oriented linguistic control strategies.
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Chang, Cheng An-Che, and 張簡安哲. "Design Fuzzy Controller by A Two-Stage Genetic Algorithm." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/18663414632317880811.
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碩士淡江大學
電機工程學系
85
It is necessary to control a system under a stable and safe state. Besides sufficiently realizing the characteristics of the system, the design of a controller is also a key point. That will improve the quality of the controller and meet with user''s requirements. Many approaches have been presented to design controllers in literatures. But most of them are often complicated. In this paper, an effective and simple algorithm, called the two-stage genetic algorithm (TSGA), is presented. It is based on the genetic algorithm and fuzzy set theory. This proposed algorithm has the following characteristics: (1) The model of the plant to be controlled is not required. (2) It is simple and easy to implement. (3) The operator''s experience can be adopted into fuzzy rules and membership function to improve the performance of the controller. (4) It has the ability to handle the multi-objective control problem and meet the operator''s requirements. (5) It allows the designer to find an acceptable non-inferior solution. From simulation results, the performance of TSGA fuzzy controller can be further improved than that of traditional fuzzy controllers. Also, it is expected that the proposed approach can be applied to other applications with good performance.
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Wang, Y. D., and 王奕敦. "Design Fuzzy Sliding Mode Controller Based on the Fuzzy Ant Colony System Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/09628816964931785592.
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碩士國立宜蘭大學
電機工程學系碩士班
97
In this paper, a novel fuzzy ant colony system (FACS) with a fuzzy mechanism and a fuzzy probable mechanism is presented for parameter determinations. Based on the fuzzy rules, the transition behavior of ants is simulated. The fuzzy probable mechanism is introduced with fuzzy probable rules to implement the diverse searching. The fuzzy probable rules are proposed to have the fuzziness in the antecedent parts and the probability in the consequent parts. To indicate the effectiveness, the fuzzy ant colony system is applied to find the proper parameters of the fuzzy sliding controllers for swinging and balancing the inverted pendulum and cart system. Also, the comparisons between the proposed fuzzy ant colony system and other ant colony optimization algorithms are provided in the simulations.
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Huang, Chih-Wei, and 黃志偉. "Design and Implementation of Genetic Algorithm Based Fuzzy Logic Controller." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/87665724364178477421.
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碩士國立成功大學
電機工程研究所
84
In this thesis, the application of the genetic algorithms (GAs) to the design of fuzzy logic controller is investigated. At first, we introduce the basic operations of GAs. An GA-based fuzzy PID controller is then proposed for the non-minimum phase system. The rule tables are self-constructed to resolve the undershoot phenomena caused by the right-half-plane zeros. The third topic is to develop a Sugeno-type fuzzy controller based on genetic algorithms. The cart-pole system is given as an illustrative example to address the feasibility of this control strategy. The proposed control algorithm can successfully make the pole upright with minimum number of rules. At last, GA- based fuzzy sliding mode control is exploited to balance the pole and regulate the cart position at the same time. A stable sliding surface which contributes better control performance is selected by genetic algorithms. Moreover, real time control of the experimental cart-pole system demonstrate the feasibility of the fuzzy control scheme.
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Chun-TeWu and 吳俊德. "PID-Like Fuzzy Controller Design Using DNA-RGA Computing Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/38138942926889237736.
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碩士國立成功大學
電機工程學系專班
98
In this thesis, a novel computing methodology called DNA-RGA computing algorithm, which combines the characteristics of DNA and Genetic algorithm, is proposed to design fuzzy controller and to improve the performance of systems. This algorithm presents a new idea to optimize the parameters and structure of controller simultaneously. DNA is known to be a chromosome string, which consists of four kind of chemical components and is able to transmit huge amount of hereditary information from generation to generation. DNA computing algorithm involves the basic and traditional operations of GA algorithms such as selection, crossover, mutation, and elite strategy. In addition, the DNA has an extra operational mechanism that includes enzyme and virus operators to provide flexibility for the structure of systems. In order to explore the major merit of DNA-RGA computing algorithm in the field of control systems, this thesis presents a variable PID-Like fuzzy controller design based on aforementioned methodology to attain the proper type of controllers, such as P-Like, PD-Like, PI-Like, or PID-Like fuzzy controllers, corresponding to different plants. Finally, the simulation results demonstrate the validity and feasibility of the proposed methodology.
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Sun, Yu-Jen, and 孫佑仁. "Optimum Design of Fuzzy Controller Using Particle Swarm Optimization Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/05058925715155879967.
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碩士國立臺北教育大學
資訊科學系碩士班
96
Invert pendulum system is a typical system with features of unstable, non-linear, and non-minimum phase. Therefore, it is always the experiment platform used for proving all kinds of control theory in the domain of control. In the thesis, we use fuzzy controller to keep the invert pendulum erective and control cart position. To design a fuzzy controller, we often need to rely on expert experience to establish the membership functions and fuzzy rules. If there is no expert experience, it must unceasingly try the failures. Therefore, considering the difficulty of establishment of membership functions and fuzzy rules, we use particle swarm algorithm to optimize the parameter of fuzzy rules and membership functions of the fuzzy controller so that we can achieve a better response. Additionally, in order to reduce the number of fuzzy rules, we reduce the number of inputs from four to two through linear combination. According to the simulation result, the proposed method in this thesis can actually make fuzzy controller work much better.
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Li, Huei-Jiun, and 李蕙君. "The Application of Genetic Algorithm Fuzzy Controller to Motor Control." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/n3a596.
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碩士國立虎尾科技大學
自動化工程研究所
103
In this dissertation, using genetic algorithm to optimize the parameters of fuzzy logic controller is proposed for motor position control. The relay feedback test method is used to obtain the mathematical model of the motor, and the integrated absolute error (IAE) used as a performance indicator to determine the parameters of the membership functions in the fuzzy controller using the genetic algorithm, in order to obtain the minimum control performance indicators. Optimal motor control performance is achieving by making improvements to the time required for parameter adjustment. The floating ball device is a nonlinear system. Combining the PD type fuzzy logic control and integral control is employed for the control law design. The integral controller improves the steady-state error of the floating ball system in order to obtain desirable results.
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Wei-Chi, Lai, and 賴偉祺. "DESIGN OF REGION-WISE FUZZY SLIDING MODE CONTROLLER WITH FUZZY TUNER USING GENETIC ALGORITHM." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/50594873131757892027.
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碩士大同工學院
電機工程研究所
86
In this thesis, a fuzzy sliding mode controller with fuzzy tuner is present. We firstly employ the sliding mode technique to design the fuzzy control rules. Secondly, according to the state values of system, the output scaling factoris adjusted by a fuzzy tuner.Then, combine the two input variables as only oneinput variable to decrease the fuzzy control rules. Finally, the genetic algorithm is applied to search the optimal parameters of the controller. The simulation results show that the proposed region-wise fuzzy sliding mode controller with fuzzy tuner using genetic algorithm has the following advantages:(1) The fuzzy rules of the fuzzy controller can be efficiently reduced.(2)The control signal is smooth. (3) The system response is fast and stable,and has the property of robustness.
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Huang, Ruei-Chia, and 黃瑞嘉. "Application of Motor Control for Genetic Algorithm based Fuzzy Grey Controller." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/86292456246436999524.
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碩士中華技術學院
機電光工程研究所碩士班
93
In this paper, we proposed a new controller, named "Genetic Algorithm based Fuzzy Grey Prediction Controller" (GFGC), which combined the convention Grey Prediction theory, Fuzzy Control theory and Genetic Algorithm. This new controller mainly has four parts, Genetic evaluation mechanism, Grey Prediction mechanism, Fuzzy Inference mechanism and Decision-making mechanism In order to promote the control effect, this new controller used the Genetic Algorithm to search the best fuzzy membership function. The present and the estimated information of system output are incorporated in the compensator, and then output of compensator is appended in control signal to improve the system performance of PID controller. Finally, via the result of simulation for motor control system, can prove that its control performance of this controller is really generally superior to other controllers, and illustrate the effectiveness of the design procedure.
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Chiang, Yen-Ko, and 江衍科. "The Design and Application of a Modified Genetic Algorithm Fuzzy Controller." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/44786006999841250879.
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碩士華梵大學
機電工程研究所
92
This thesis presents two new strategies, fission evolution and coercion evolution, to modify the traditional genetic algorithm (GA) such that the learning ability of the GA algorithm can be improved. The modified GA algorithm is then applied to the design of fuzzy controller in order to demonstrate the effectiveness of the proposed approach. One of the main spirits of GA algorithm is to prevent the local optimization problem. It can be easily designed without an expert''s knowledge base. So how to increase the learning speed and learning performance of GA algorithm has received much attention and become an important research field recently. The main contribution of this thesis is to propose two modified learning mechanisms, the fission evolution and coercion evolution, for the improvement of learning speed and the search of better fitness value. In order to prove the feasibility of the modified approaches, the proposed algorithm is applied to the design of PID controller and fuzzy controller. It is shown by both computer software simulation and DC motor hardware experiment that the modified GA algorithm can find better fitness value with faster convergent rate. The control performances of the closed loop system are greatly improved especially when compared with those by traditional GA learning algorithm.
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Syu, Bo-yu, and 許博喻. "Design of Linguistic Hedge Fuzzy Logic Controller with Heuristic-Algorithm Enhancements." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/2w84g6.
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碩士國立雲林科技大學
電機工程系碩士班
96
In this thesis, four heuristic algorithms are applied to design the linguistic-hedge fuzzy logic controller. These algorithms include genetic algorithm, guided simulated annealing algorithm, guided ant algorithm, particle swarm optimization algorithm. For the controller, we apply different partition strategies on the input-variable domains and analyze the effects on the performance of the controller. The input-variable domains are partitioned in the following manners: the uniform sub-intervals, the more uniform sub-intervals, the fewer uniform sub-intervals, and the non-uniform sub-intervals. The simulation results on the truck-backer upper control system show better performance of the linguistic-hedge fuzzy logic controller. Consider the performances of different strategies. The linguistic-hedge fuzzy logic controller having the input-variable domains with 16 uniform sub-intervals associated with guided simulated annealing algorithm has the best performance. The number of iterations is 1028; the docking error is 0.04.
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Chu, I.-Chieh, and 朱毅傑. "Application of Fuzzy Controller with RCE Algorithm to Bilateral Ancillary Services." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/58939898787125837587.
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碩士國立成功大學
電機工程學系碩博士班
93
In the ongoing trend of restructuring the electricity market worldwide, Ancillary Services are introduced for operating entities to realize the market functions and maintain system security. The generation providers willing to join the regulation markets would be interested in how the direct regulation service between supplier and customer be effectively achieved at lower costs, while operating quality can be well maintained. This thesis evaluates a customer based Regulation Control Error (RCE) that is used for operating entities to provide inner-area or inter-area regulation service. Test results showing the proposed method in the simulated condition performed as desired. This thesis also proposes the fuzzy gain scheduling controller that was specifically used to perform the bilateral regulation service. The effectiveness of improving the bilateral regulation performance by the proposed adaptive controller is also confirmed.
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Wen, I.-Cian, and 溫翌茜. "Genetic Algorithm Tuned Fuzzy Logic Controller for the Rotary Inverted Pendulum." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/49225687734667674633.
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碩士元智大學
電機工程學系
98
In this thesis, a genetic algorithm (GA) is adopted to search optimal means of input membership functions of a fuzzy logic controller. With the optimal membership function been searched, the fuzzy logic controller can control efficiently a single-input single-output rotary inverted pendulum. The advantage of proposed method is to tune the membership functions automatically rather than to tune the functions by trial and error. The parameters of membership functions are converted to a chromosome which is encoded in a binary string. Because the membership functions are symmetric to zero, the length of each chromosome could be reduced in half that the computation time will be also shorter with the short chromosomes. Moreover, we choose roulette wheel selection as reproduction operator, one-point crossover operator, and classical mutation operator. After genetic algorithm completes searching for optimal parameters, the optimal membership function will be introduced to fuzzy logic controller. Finally, simulation results show that the proposed GA-tuned fuzzy logic controller is effective for the rotary inverted pendulum system to track a trajectory.
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Tsung-ChengYang and 楊宗晟. "Fuzzy Controller Design by Artificial DNA Assisted Queen Bee Genetic Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/75322761035258220893.
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碩士國立成功大學
電機工程學系專班
101
This thesis proposes an artificial DNA assisted queen bee genetic algorithm (DNA+QBGA) to learn the gains, control structures, membership functions, and rules of the fuzzy controller. The queen bee genetic algorithm (QBGA) possesses simple and fast evolution process to figure out the best parameters and the DNA computing is adopted to determine the structure of fuzzy controller. Each fuzzy control structure can be defined by a different bee hive, which contains the control structure and dimension of the gain. The presented DNA+QBGA can make the membership functions and rules communicate with one another among different control structures. Moreover, a novel three-step crossover operation is investigated such that the crossover between different odd dimensions of membership functions can be made. Step one is that the dimensions of parents (queen and drone) and the offspring (brood) are expanded to the same dimension resolved by their least common multiple. Step two is to randomly select the genes from the parents in the corresponding space. Step three is that the offspring gene is calculated by the real-coded crossover between their parents. Finally, the simulation results of the fuzzy controlled cart-pole and chaotic systems demonstrate the feasibility and effectiveness of the proposed schemes.
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Wang, Lui. "Genetic algorithm tuning of a fuzzy logic controller for a dynamic system." Thesis, 1995. http://hdl.handle.net/1911/14004.
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This thesis describes the use of the genetic algorithm to facilitate the design process of a fuzzy logic based controller. The basic mechanics of both the fuzzy logic control system and the genetic algorithm are presented for establishing the foundation of this research. A detailed design process to integrate the fuzzy logic controller and the genetic algorithm is disclosed. Software is developed to simulate the dynamics of a two-link planar manipulator, and a multiple-link fuzzy logic control (MLFLC) system is developed to control a non-linear robotics system. In this work, the genetic algorithm technique is used to design both the Universe of Discourse of some of the control variables and also the shape and location of membership functions. As a result, the system is able to maintain control with moderate errors. The most important contribution of this work is that it demonstrates the effectiveness of using genetic algorithms to optimize fuzzy logic control systems.
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Li, Bo-Yu, and 李柏煜. "Design of Linguistic Hedge Fuzzy Logic Controller Enhanced by Bacterial Evolutionary Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/70932544554012485044.
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碩士國立雲林科技大學
電機工程系碩士班
97
In this thesis, we design a linguistic-hedge fuzzy logic controller enhanced by bacterial evolutionary algorithm. The bacterial evolutionary algorithm is applied to search the better linguistic-hedge combination vectors for modifying the membership functions such that the controller can achieve a better performance. We tried to improve the performance of this controller by modifying the intervals and the shapes of the membership functions. For the intervals, we partitioned the membership functions into 16 uniform intervals, 16 nonuniform intervals, 32 uniform intervals, and 8 uniform intervals. For the shapes, we applied the linguistic-hedge combination vectors obtained from the bacterial evolutionary algorithm for modifying the shape of membership functions. The simulation results show that this design does perform well.
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黃法乾. "Adaptive fuzzy controller system design with PI learning algorithm and its implementation." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/83148376665193674950.
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碩士中華大學
電機工程學系(所)
96
Due to the complexity of the practical control system, it is hard to design a controller based on the traditional linear control technique. This study proposes some intelligent control schemes which combines the advantages of sliding mode control, adaptive control and fuzzy control. It is referenced as adaptive fuzzy (sliding-mode) controller. The controller parameters of the proposed controller schemes can be on-line tuned based on the Lyapunov theorem, thus the stability of the closed-loop system can be guaranteed. In order to increase the convergence speed of tracking error and controller parameters, the adaptive law is redesigned in a proportion-integral type form. Finally, the proposed intelligent control algorithm (adaptive fuzzy control and adaptive fuzzy sliding-mode control system with PI-type learning algorithm) are applied to a chaotic dynamic system and a DC-DC converter. From the simulation and experimental results, it can show that the developed proportion-integral-type learning algorithm can achieve better performance than integral-type learning algorithm. Keywords: Adaptive control, Fuzzy control, Sliding mode control, Chaotic dynamic system, DC-DC converter.
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Yin, Shean-Tay, and 尹顯泰. "Study on the Genetic Algorithm and Application in the Fuzzy Controller Design." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/15365588160186013398.
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碩士國立成功大學
電機工程學系
87
Fuzzy control has been applied to various industrial processes; however, its control rules and membership functions are usually obtained by trial-and-error. In this thesis, we first find various parameters of the optimization algorithm for efficiency by genetic algorithm. Next, we design for control rules simultaneously by a genetic algorithm (GA). GAs is search algorithms based on the mechanics of natural selection and natural genetics. They are easy to implement and efficient for multivariable optimization problems such as fuzzy controller design. The simulation result shows that the fuzzy controller thus designed can achieve a good performance merely by using a few fuzzy variables.
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Lu, chun-ming, and 盧俊明. "Fuzzy Controller Design by Ant Colony Optimization Algorithm And Its Software/Hardware Implementation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/31186477534410255350.
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碩士國立中興大學
電機工程學系
93
This thesis proposes the application of Ant Colony Optimization (ACO) algorithm to design the consequent parts of a fuzzy controller. This is called ACO-FC. The ACO-FC that is improved design efficiency and control performance of main objectives. For a fuzzy controller, we partition the antecedent part in grid-type, and then list all candidate consequent values of the rules. The path of an ant is regarded as one combination of consequent values selected from every rule. Searching of the best one among all combinations is based on thickness of the pheromone of ACO. Performance of the proposed method has been shown to be better than genetic algorithm on simulations of cart-pole balancing and temperature control problems. The used ACO is hardware-implemented on FPGA (Field Programmable Gate Array) chip. The implemented chip contains one memory unit for depositing thickness of pheromone, one random number generator of 16 bits, one 16 bits divider, and some other logic operation units. To verify the performance of the chip, we have applied it on simulation of water bath temperature control. For reinforcement fuzzy controller design problem, we propose the incorporation of Fuzzy-Q learning into ACO, called FQ-ACO, to further improve the performance of ACO. For all the candidates in the consequent part of a rule, we assign each one a corresponding Q-value. Update of the Q-value is based on fuzzy-Q learning. The best combination of consequent values of a fuzzy controller is searched according to both pheromone and Q-value. To verify the performance of FQ-ACO, reinforcement fuzzy control of water bath temperature control system, magnetic levitation control system, and truck back-upper control are simulated. Simulations on the three problems with ACO alone and fuzzy-Q alone are also performed, respectively. Performance of FQ-ACO is verified from the comparisons.
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Chang, Shin-Han, and 張仕翰. "Study of Power System Stabilizer implemented with Fuzzy PID Controller and Genetic Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/02182705150548673093.
Full textAbstract:
碩士國立臺北科技大學
電機工程系碩士班
91
In recent years, because the demand on power usage increases continuously and new power plants and substations are not easy to build on account of costs and regulations, Taipower system has encountered two major problems：power generation is approaching to its maximum capacity and transmission system experiences overloaded situation. These problems could cause the power system experiences unstable and thus susceptible to disturbance. As a result, some component of the power system could malfunction and more seriously, even the whole power system could collapse. Therefore, controlling the stability of power system is the most important objective in providing stable and uninterrupted electric power to the end users and hence is the primary research goal of this thesis. This thesis proposes a genetic fuzzy PID control scheme incorporated with genetic algorithms to design the power system stabilizer. This method is based on the conventional PID controller. Since the values of KP, KI, KD parameters of the conventional controller have to be determined before the control starts, this sort of controller will not adapt to changing of the target system. Therefore, we employ a fuzzy PID controller to deal with this problem. In addition, genetic algorithms are applied to find the optimal membership functions for the fuzzy rules used in the fuzzy PID controller. Finally, the simulated single-machine system is reduced to a less complicated model with two state variables involved by the optimal order-reduced model approach to simplify the design of the controller.
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Chiu, Mu Hsiang, and 邱睦翔. "Design and Implementation of Multi-robot Formation Controller Based on Fuzzy Consensus Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/66402259505532637556.
Full textAbstract:
碩士長庚大學
電機工程學系
98
In this thesis, the formation control of a multi-robot system is mainly addressed, where the essential idea is relied on a fuzzy consensus algorithm. To avoid collisions, a distance-dependent collision avoidance mechanism is proposed such that the required multi-robot formations can be achieved as well. Finally, to evaluate he feasibility of the proposed algorithm, the Webots simulation software and the e-Puck robots are adopted for the theoretic evaluation and experimental testing, respectively. From the simulation and experimental results, the proposed fuzzy consensus algorithm can provide better formation performance than the counterpart of conventional consensus algorithms subject to different communication topologies and formation patterns.
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Shi-WenPan and 潘世文. "A Hybrid of Genetic Algorithm and Ant Colony Optimization for Fuzzy controller Design." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/84568342064432181723.
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碩士國立成功大學
電腦與通信工程研究所
98
Searching for the combination of consequent values of neural fuzzy systems in automatic fuzzy system design is a Optimization Problem .And there are a lots of solutions method for it. The ant colony optimization algorithm(ACO) has a good performance on convergent but it is easy to get the local optima. In addition, the solution optimization problem uses genetic algorithm (GA)usually because genetic algorithm has a good capability of global searching but it`s performance is lower than ACO. If we can combine GA and ACO to take their advantages it will be a good problem to study. In this paper, we propose the combination of GA and ACO method. The Objective is to consolidate global search of GA and local search of ACO. The solution from ACO is replaced by the best solution from GA evolution when ACO convergent to local optima, let Consequent values of fuzzy controller be accurate and stable. This paper uses a hybrid of genetic algorithm and ant colony optimization for fuzzy controller in simulation. We search for problem consequent values of neural fuzzy. we will compare with other different algorithm finally.
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Lin, Chia-Tseng, and 林家增. "THE STUDY ON FUZZY NEURAL NETWORK CONTROLLER USING ARTIFICIAL IMMUNE BACK-PROPAGATION ALGORITHM." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/90209604944539874518.
Full textAbstract:
碩士大同大學
電機工程學系(所)
99
A fuzzy neural network (FNN) identifier based on back-propagation artificial immune (BPIA) algorithm, named the FNN-BPIA controller, is proposed for the nonlinear systems in this thesis. The proposed controller is composed of an FNN identifier, an IA estimator, a hitting controller, and a computation controller. Firstly, The FNN identifier is utilized to estimate the dynamics of the nonlinear system. These parameters which include weights, means, and standard deviations of the FNN identifier are adjusted by the BP algorithm. Secondly, the initial values which include weights, means, and standard deviations of the FNN identifier and the parameters of the BP algorithm are estimated by the IA estimator. Thirdly, the training process of the IA estimator has four stages which include initialization, crossover, mutation, and evolution. Further, the computation controller is given to calculate the control effect and the hitting controller is utilized to eliminate the uncertainties. Finally, the inverted pendulum system and the second-order chaotic system are simulated to verify the performance and the effectiveness of the FNN-BPIA controller.
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Sheng, Tang Tien, and 唐天生. "The Studies of A Neural Network Fuzzy Controller and A Grey Back Propagation Algorithm." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/18005035464066460461.
Full textAbstract:
博士國防大學中正理工學院
國防科學研究所
92
This article is consisted of two subjects. The first one is the study of neural network fuzzy controller; the second is the study of grey back propagation（GBP）algorithm. First, this paper presents two learning methods for automatically generating fuzzy if-then rules in the neural network fuzzy controller. One is the combining heuristic method with back propagation（BP）algorithm method; the other is the hybrid neural network learning method. Through computer simulations, the proposed two methods are shown to have following advantages: （1）It is unnecessary to rely on experts or experienced human to acquire fuzzy rules. （2）It does require neither time-consuming iterative learning procedures nor complicated rule generation mechanisms. （3）The obtained fuzzy rules have self-learning and robust capabilities. The second objective of this paper is to use BP algorithm in conjunction with grey relationship in order to improve BP algorithm. This new technique is developed by directly incorporating grey relationship into BP algorithm, and a grey BP learning method, namely the GBP, is proposed. Furthermore, the GBP can effectively learn neural network.