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Dissertations / Theses on the topic 'Fuzzy Control'
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1
Gormandy, Brent Anthony. "Fuzzy model predictive control." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248858.
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Hoyle, W. J. "Fuzzy logic, control and optimisation." Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6458.
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This thesis examines the utility of fuzzy logic in the field of control engineering. A tutorial introduction to the field of fuzzy control is presented during the development of an efficient fuzzy controller. Using the controller as a starting point, a set of criteria are developed that ensure a close connection between rule base construction and control surface geometry. The properties of the controller are exploited in the design of a global controller optimiser based on a genetic algorithm, and a tutorial explaining how the optimiser may be used to effect automatic controller design is given. A library of software that implements a fast fuzzy controller, a genetic algorithm, and various utility routines is included.
3
Chowdhury, Mina Munir-ul Mahmood. "Evolutionary and reinforcement fuzzy control." Thesis, University of Glasgow, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299747.
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Layne, Jeffery Ray. "Fuzzy model reference learning control." Connect to resource, 1992. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1159541293.
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Moore, Christopher G. "Indirect adaptive fuzzy controllers." Thesis, University of Southampton, 1992. https://eprints.soton.ac.uk/250154/.
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Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and time--varying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, self--learning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as a possible solution to this problem. This controller is then developed by combining on--line adaptive modelling with model causality inversion and on--line controller design. The resulting controller is an analogue of the indirect adaptive algebraic controller. A major advantages of this method is the separation of model convergence and control loops enabling the two aspects to be analysed separately. Demonstration of this work has been achieved by a series of simulation tests using a variety of vehicle models. A conventional front wheel steer road vehicle model has been used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the properties of the controller. To test the controller with realistic demand signals, a static rule-based piloting system has also been developed. These simulations have demonstrated i) the successful control of systems with little a priori vehicle knowledge ii) ability to adapt to continuous and sudden parametric changes in the process iii) good noise rejection properties iv) good disturbance rejection properties and v) ability to adapt to stationary loop non--linearities.
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Hu, Jian-Quan. "Adaptive fuzzy predictive control using a neuro-fuzzy model with application to sintering." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265575.
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Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.
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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.
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Fung, Yun-hoi. "Linguistic fuzzy-logic control of autonomous vehicles /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19660583.
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Ali, Agha Rehmat. "Predicted Speed Control based on Fuzzy Logic for Belt Conveyors : Fuzzy Logic Control for Belt Conveyors." Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-70106.
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In order to achieve energy savings for belt conveyor system, speed control provides one of the best solutions. Most of the traditional belt conveyors used in the industries are based on constant speed for all operational times. Due to the need and advancements in technology, Variable Frequency Drives (VFD) are employed in industries for a number of processes. Passive Speed Control was previously suggested for the proper utilization of VFD to make belt conveyor systems more power e- cient with increased life expectancy and reduced environmental eects including the noise reduction caused by constant speed of operation. Due to certain conditions and nature of operation of belt conveyor systems, it is not desirable to use Passive Speed control where feeding rate is random. Due to the extreme non-linearity of the random feeding rate, an Active speed control for VFD is desired which adjusts belt speed according to the material loading. In this thesis an Active Speed control for VFD is proposed that can achieve energy and cost ecient solutions for belt conveyor systems as well as avoiding half-lled belt operations. The aim of this thesis work is primarily to determine reliability and validity of Active Speed Control in terms of power savings. Besides achieving power savings, it is also necessary to check the economic feasibility. A detailed study is performed on the feasibility of Active Speed Control for random feeding rate according to industrial requirements. Due to the random and non-linearity of the material loading on the belt conveyor systems, a fuzzy logic algorithm is developed using the DIN 22101 model. The developed model achieves Active Speed Control based on the feeding rate and thereby optimizes the belt speed as required. This model also overcomes the risks of material spillage, overloading and sudden jerks caused due to unpredicted rise and fall during loading. The model conserves 20- 23% of the total power utilized compared to the conventional conveyor systems in use. However it is noticed that the peak power of conventional conveyor belt systems is up to 16% less compared to the proposed model. If implemented in dierent industries, based on the operational time and total consumption of electricity, the proposed Active speed control system is expected to achieve economic savings up to 10-12 % .
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Ellis, Susan Marie. "Fuzzy control and an evaluation of the self-organizing fuzzy controller." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/45944.
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<p>Fuzzy control is a rule based type of control that aims to imitate the human's ability to
express a control policy using linguistic rules, and to reason using those rules to control a system. Fuzzy control is nonlinear and not dependent on a precise mathematical description of the plant, and is therefore more easily applied to systems such as industrial processes that are hard to
model. An overview is given of the fuzzy controller, along with descriptions of applications and
theoretical approaches to designing and analyzing the controller.</p>
<p>The selfâ organizing controller is able to generate or modify its rules in real time based on
the system performance. It was tested to determine how well it was able to learn a quality
control policy. The selfâ organizing controller was found to exhibit poor steady state performance,
and to be equally likely to learn poor control as to learn good control. It was not found to
eliminate the need for careful tuning of the controller parameters and gains.</p><br>Master of Science
11
Kadmiry, Bourhane. "Fuzzy Control for an Unmanned Helicopter." Licentiate thesis, Linköping University, Linköping University, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5723.
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<p>The overall objective of the Wallenberg Laboratory for Information Technology and Autonomous Systems (WITAS) at Linköping University is the development of an intelligent command and control system, containing vision sensors, which supports the operation of a unmanned air vehicle (UAV) in both semi- and full-autonomy modes. One of the UAV platforms of choice is the APID-MK3 unmanned helicopter, by Scandicraft Systems AB. The intended operational environment is over widely varying geographical terrain with traffic networks and vehicle interaction of variable complexity, speed, and density.</p><p>The present version of APID-MK3 is capable of autonomous take-off, landing, and hovering as well as of autonomously executing pre-defined, point-to-point flight where the latter is executed at low-speed. This is enough for performing missions like site mapping and surveillance, and communications, but for the above mentioned operational environment higher speeds are desired. In this context, the goal of this thesis is to explore the possibilities for achieving stable ‘‘aggressive’’ manoeuvrability at high-speeds, and test a variety of control solutions in the APID-MK3 simulation environment.</p><p>The objective of achieving ‘‘aggressive’’ manoeuvrability concerns the design of attitude/velocity/position controllers which act on much larger ranges of the body attitude angles, by utilizing the full range of the rotor attitude angles. In this context, a flight controller should achieve tracking of curvilinear trajectories at relatively high speeds in a robust, w.r.t. external disturbances, manner. Take-off and landing are not considered here since APIDMK3 has already have dedicated control modules that realize these flight modes.</p><p>With this goal in mind, we present the design of two different types of flight controllers: a fuzzy controller and a gradient descent method based controller. Common to both are model based design, the use of nonlinear control approaches, and an inner- and outer-loop control scheme. The performance of these controllers is tested in simulation using the nonlinear model of APID-MK3.</p><br>Report code: LiU-Tek-Lic-2002:11. The format of the electronic version of this thesis differs slightly from the printed one: this is due mainly to font compatibility. The figures and body of the thesis are remaining unchanged.
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Lei, Kam Kin. "Fuzzy control on double inverted pendulum." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445842.
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Farah, Hassan. "The fuzzy logic control of aircraft." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ43339.pdf.
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Marriott, Jack. "Adaptive robust fuzzy logic control design." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15819.
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Po-ngaen, Watcharin. "Neuro-fuzzy control in tele-robotics." Thesis, University of Newcastle Upon Tyne, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430351.
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Gao, Yang. "Adaptive fuzzy control of nonlinear systems." Mémoire, Université de Sherbrooke, 2006. http://savoirs.usherbrooke.ca/handle/11143/1335.
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Fuzzy logic provides human reasoning capabilities to capture uncertainties that cannot be described by precise mathematical models. An adaptive fuzzy system is a fuzzy logic system equipped with a learning algorithm. A"learning system" possesses the capability to improve its performance over time by interacting with its environment, so an adaptive control system has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. This thesis proposes a fast approach for system modeling by neuro-fuzzy networks (NFNs), which can successfully model the nonlinear system dynamics and its uncertainties. This algorithm can construct a system model by NFN, i.e., fuzzy rules can be generated automatically in the learning process from training data without partitioning the input space and selecting initial parameters a priori. This thesis presents an adaptive fuzzy control method of nonlinear systems using the NFN controller, which can be constructed by the fast learning algorithm proposed in this thesis. In simulation studies, an inverted pendulum system can track the desired trajectory very well and the control system has good robustness to disturbances using the adaptive control method proposed. The inverted pendulum is controlled by the proposed adaptive fuzzy control method, classical PID control method and nonadaptive fuzzy control method respectively; the control results show that the adaptive fuzzy control system has the best performances among the three control systems in terms of transient and steady-state results.
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Wang, Jian Zhou. "Robust control with fuzzy logic algorithms." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13195.
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This thesis presents the results of an investigation of the robustness of the widely used Mandani-type fuzzy logic control systems under a wide variation of parameters of the controlled process. The measurements of the dynamic performance and system robustness of a control system were firstly defined from the engineering point of view, and the concepts of the robust space and the robustness index were introduced. The robustness of the FLC systems was investigated by analyzing the structure of the fuzzy rule base and membership functions of the input-output variables. Based on the close relation of the fuzzy rule base and the system dynamic trajectory on the phase plane, a switching line method is introduced to qualitatively analyze the dynamic performance of the SISO FLC systems. This switching line method enables the qualitative prediction of the shape and position of the robust space of the FLC controlled first order processes and second order processes. The effects of FLC parameters on system robustness were also investigated. The movements of the position and the shape of the switching line with the variation of the controller parameters were analyzed, and its relation with the system performance was reported. Three methods were proposed to improve the robustness of the FLC system. The first design method proposed was based on the switching line characteristics of the FLC system. The second method, called phase advanced FLC, was introduced to handle the control of high order processes with fuzzy algorithms. The third method was an evolutionary method based on the genetic algorithm which was used to automatically design a robust fuzzy control system, assuming the availability of the controlled process model.
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Dadone, Paolo. "Fuzzy Control of Flexible Manufacturing Systems." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36531.
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<p> Flexible manufacturing systems (FMS) are production systems consisting of
identical multipurpose numerically controlled machines (workstations), automated
material handling system, tools, load and unload stations, inspection stations, storage
areas and a hierarchical control system. The latter has the task of coordinating and
integrating all the components of the whole system for automatic operations. A
particular characteristic of FMSs is their complexity along with the difficulties in
building analytical models that capture the system in all its important aspects. Thus
optimal control strategies, or at least good ones, are hard to find and the full potential
of manufacturing systems is not completely exploited.</p><p>
The complexity of these systems induces a division of the control approaches
based on the time frame they are referred to: long, medium and short term. This thesis
addresses the short-term control of a FMS. The objective is to define control strategies,
based on system state feedback, that fully exploit the flexibility built into those systems.
Difficulties arise since the metrics that have to be minimized are often conflicting and
some kind of trade-offs must be made using "common sense". The problem constraints
are often expressed in a rigid and "crisp" way while their nature is more "fuzzy" and
the search for an analytical optimum does not always reflect production needs. Indeed,
practical and production oriented approaches are more geared toward a good and
robust solution.</p><p>
This thesis addresses the above mentioned problems proposing a fuzzy scheduler
and a reinforcement-learning approach to tune its parameters. The learning procedure
is based on evolutionary programming techniques and uses a performance index that
contains the degree of satisfaction of multiple and possibly conflicting objectives. This
approach addresses the design of the controller by means of language directives coming
from the management, thus not requiring any particular interface between management
and designers.</p><p>
The performances of the fuzzy scheduler are then compared to those of
commonly used heuristic rules. The results show some improvement offered by fuzzy
techniques in scheduling that, along with ease of design, make their applicability
promising. Moreover, fuzzy techniques are effective in reducing system congestion as
is also shown by slower performance degradation than heuristics for decreasing inter-
arrival time of orders. Finally, the proposed paradigm could be extended for on-line
adaptation of the scheduler, thus fully responding to the flexibility needs of FMSs.</p><br>Master of Science
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Cook, Brandon M. "Multi-Agent Control Using Fuzzy Logic." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447688633.
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Farah, Hassan (Hassan Kahiye) Carleton University Dissertation Engineering Mechanical and Aerospace. "The Fuzzy logic control of aircraft." Ottawa, 1999.
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BRITO, Thiago Souza Pereira de. "Desenvolvimento de um controlador PID-Fuzzy para o controle de nível de água de um pressurizador de um reator nuclear." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16625.
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Previous issue date: 2015-08-31<br>Capes<br>Usinas nucleares são sistemas físicos de natureza não-linear. E, devido as grandes variações de potência no regime de trabalho, um regime transitório se desenvolverá onde a pressão e a temperatura média do circuito primário irão sofrer variações significativas. Com a finalidade de inibir variações de pressão e manter o sistema pressurizado, o Pressurizador tem a função de controlar as variações que ocorrem no sistema primário do reator. Este trabalho tem por objetivo o desenvolvimento de um Controlador PID (Proporcional Integral Derivativo) baseado em lógica Fuzzy para atuação em um Pressurizador de um Reator Nuclear de Água Pressurizada. Um Controlador Fuzzy foi desenvolvido através do processo de fuzzificação, inferência e defuzzificação das variáveis de interesse para um Pressurizador, em seguida este controlador foi acoplado a um Controlador PID formando um Controlador PID regido pela lógica Fuzzy, ou seja, um Controlador PID-Fuzzy. Este foi, validado experimentalmente em uma Planta de Simulações no qual foram atribuídos transitórios semelhantes à um Pressurizador de um Reator Nuclear, observado e ajustado para melhores respostas e resultados. Os resultados desta validação foram comparados com Controladores Simples (on/off) e PID, também de forma experimental. Após a validação o Controlador PID-Fuzzy foi desenvolvido para atuar no Código MODPRESS, que simula matematicamente o Pressurizador NEPTUNUS de um Reator. Neste Código também foi feita uma comparação entre os Controladores PID e o Controlador PID-Fuzzy. Os resultados obtidos demonstraram que o Controlador PID-Fuzzy apresentou melhor desempenho e precisão, com respostas suaves, o que representa menor stress mecânico, agregando maior robustez na condução e controle do Pressurizador, dando mais confiabilidade e segurança no Reator Nuclear<br>Nuclear power plants are physical systems of nonlinear nature. Because of large power variations in operational conditions, transient situations will develop where the pressure and temperature average of the primary circuit coolant will undergo significant variations. In order to inhibit pressure fluctuations and maintain the pressurized system, the Pressurizer has the function of controlling the variations that occur in the primary reactor system coolant. This work aims at the development of a PID controller (Proportional Integral Derivative) based on fuzzy logic to operate in a Pressurizer of a Nuclear Pressurized Water Reactor. A fuzzy controller was developed using the process of fuzzification, inference and defuzzification of the variables of interest for a Pressurizer then this controller was connected to a PID controller forming a PID controller, but driven by fuzzy logic, that is, a PID-Fuzzy controller. This was validated experimentally on a plant simulations in which transients were assigned similar to the one Pressurizer of a Nuclear Reactor, observed and adjusted for best results. The results of this validation were compared with Simple Controllers (on/off) and PID also on an experimental basis. After validation the PID-Fuzzy Controller is designed to operate in MODPRESS Code, which mathematically simulates a Pressurizer NEPTUNUS of a reactor. In this code a comparison between PID and PID-Fuzzy Controllers was also made. The results showed that the PID-Fuzzy Controller demonstrated better performance and accuracy, with smooth responses, which means less mechanical stress, adding more robustness in the conduction and control of Pressurizer, giving more reliability and safety in Nuclear Reactor.
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GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention
al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete
rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.
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馮潤開 and Yun-hoi Fung. "Linguistic fuzzy-logic control of autonomous vehicles." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29812690.
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Zhou, Yiming. "Knowledge-based real-time linguistic fuzzy controllers." Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303780.
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Onofre, Filho Marc?lio de Paiva. "L?gica Fuzzy para Controle de pH em um Processo Petrol?fero." Universidade Federal do Rio Grande do Norte, 2011. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15361.
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Previous issue date: 2011-09-02<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>This work proposes the design, the performance evaluation and a methodology for tuning the
initial MFs parameters of output of a function based Takagi-Sugeno-Kang Fuzzy-PI controller
to neutralize the pH in a stirred-tank reactor. The controller is designed to perform pH
neutralization of industrial plants, mainly in units found in oil refineries where it is strongly
required to mitigate uncertainties and nonlinearities. In addition, it adjusts the changes in pH
regulating process, avoiding or reducing the need for retuning to maintain the desired
performance. Based on the Hammerstein model, the system emulates a real plant that fits the
changes in pH neutralization process of avoiding or reducing the need to retune. The
controller performance is evaluated by overshoots, stabilization times, indices Integral of the
Absolute Error (IAE) and Integral of the Absolute Value of the Error-weighted Time (ITAE),
and using a metric developed by that takes into account both the error information and the
control signal. The Fuzzy-PI controller is compared with PI and gain schedule PI controllers
previously used in the testing plant, whose results can be found in the literature.<br>Prop?em-se neste trabalho a concep??o, a avalia??o do desempenho e uma metodologia para
sintonia dos par?metros iniciais das fun??es de pertin?ncia de sa?da de um controlador Fuzzy-
PI, tipo Takagi-Sugeno-Kang, para o acompanhamento de refer?ncias de pH em um tanque
reator com agita??o cont?nua. O controlador ? projetado para executar a neutraliza??o do pH
em plantas industriais, principalmente em unidades encontradas em refinarias de petr?leo. O
sistema emula, com base no modelo de Hammerstein, uma planta real que se ajusta ?s
mudan?as no processo de neutraliza??o do pH, evitando ou reduzindo a necessidade de
ressintonia. O desempenho do controlador ? avaliado pelos overshoots, pelos tempos de
acomoda??o, pelos ?ndices Integral do valor absoluto do erro (IAE) e Integral do valor
absoluto do erro com pondera??o do tempo (ITAE), e atrav?s de um ?ndice desenvolvido por
Goodhart que leva em considera??o tanto informa??es do erro quanto do sinal de controle. O
controlador Fuzzy-PI ? comparado com controladores PI e PI Escalonado utilizados
previamente na planta de teste, cujos resultados est?o dispon?veis na literatura.
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García, Z. Yohn E. "Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.
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Feng, Yi. "Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3286.
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Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.
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Khiyo, Sargon, University of Western Sydney, of Science Technology and Environment College, and School of Engineering and Industrial Design. "Neuro/fuzzy speed control of induction motors." THESIS_CSTE_EID_Khiyo_S.xml, 2002. http://handle.uws.edu.au:8081/1959.7/554.
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The thesis involved the design, implementation and testing of a second order neuro-fuzzy controller for the speed control of an AC induction motor, and a comparison of the neuro-fuzzy controller's performance with that of the PI algorithm. It was found experimentally, that the operating temperature of the AC induction motor affected the ability of the PI controller to maintain the set speed. The linear PI algorithm approximation was observed to produce transient speed responses when sudden changes in load occurred. The neuro-fuzzy design was found to be quite involved in the initial design stages. However, after the initial design, it was a simple matter of fine-tuning the algorithm, to optimize performance for any parameter variations of the motor due to temperature or due to sudden changes in load. The neuro-fuzzy algorithm can be developed utilising one of two methods. The first method utilises sensor-less control by detailed modeling of the induction motor; where all varying parameters of the motor are modeled mathematically. This involves using differential equations, and representing them in the form of system response block diagrams. When the overall plant transfer function is known, a fuzzy PI algorithm can be utilised to control the processes of the plant. The second method involves modeling the overall output response as a second order system. Raw data can then be generated in a text file format, providing control data according to the modeled second order system. Using the raw data, development software such as FuzzyTECH is utilised to perform supervised learning, so to produce the knowledge base for the overall system. This method was utilised in this thesis and compared to the conventional PI algorithm. The neuro-fuzzy algorithm implemented on a PLC was found to provide better performance than the PI algorithm implemented on the same PLC. It provided also in the added flexibility for further fine-tuning and avoided the need for rigorous mathematical manipulation of linear equations<br>Master of Engineering (Hons)
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Mian, Tariq M. "Fuzzy Logic based Automotive Airbag Control System." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ52612.pdf.
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Su, Yi-Min SuYi-Min SuYi-Min, and 蘇益民. "Observer-based H∞ Fuzzy Control Design-Hybrid Taguchi-Genetic Fuzzy Control Approach." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54025243201400377529.
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碩士<br>國立高雄應用科技大學<br>電機工程系<br>99<br>This thesis deals with observer-based H∞ control problem for T-S fuzzy systems .
By using Lyapunov stability analysis as the basis for derivation. literature on the observer-based control issue will encounter nonlinear matrix inequalities, must be solved by two-step procedure, lead to the solution set is too conservative. In this thesis, by using hybrid Taguchi genetic algorithm to search optimization controller gain and observer gain, the unknown controller gain and observer gain is assumed as nonvariable, the nonlinear matrix inequalities into linear matrix inequalities, avoid the shortcomings of two-step procedure.
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Freitas, Joao Miguel Malva. "Evolutionary Learning of Fuzzy Controllers." Master's thesis, 2018. http://hdl.handle.net/10316/86374.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>Controladores inteligentes são hoje um importante aspecto no controlo de processos industriais e, particularmente, o controlador de lógica difusa com capacidades de aprendizagem é um caso de estudo muito interessante, devido ao seu formato e características únicas.Com o intuito de controlar sistemas com múltiplas entradas e uma saída foram estudados algoritmos de controlo difuso com uma componente adaptativa, por outras palavras, com a capacidade de adaptar a regras e parâmetros existentes no controlador e com uma componente evolutiva, por outras palavras, com a capacidade de modificar a estrutura do controlador com a adição de novas regras, obtidas através do uso de informação da saída e entradas do sistema. Adicionalmente, o controlador deve ser capaz de alterar sua estrutura ao mesmo tempo que controla o sistema, sem necessidade de treino prévio, e também controlar sistemas desconhecidos sem conhecimento do modelo e dinâmica do sistema. Após algumas pesquisas foi escolhido um algoritmo com as características mencionadas que serviu de base para o algoritmo apresentado nesta dissertação.Neste trabalho são apresentados os fundamentos de Controladores Difusos, a arquitectura e funcionamento do algoritmo proposto, sendo mencionado as melhorias às falhas detectadas do algoritmo original que foi estudado. A importância e influência de vários parâmetros do algoritmo proposto são também analisados em detalhe.De forma a validar e demonstrar a capacidade do algoritmo proposto, foi testado e analisado o seu desempenho no controlo de diversos sistemas simulados com múltiplas entradas e uma saída, assim como num sistema real composto por dois motores DC acoplados. Em todos os sistemas testados foram induzidas perturbações, tendo sido analisada a resposta do algoritmo proposto.<br>Nowadays, intelligent controllers are an important aspect in the control of industrial processes and the particular Fuzzy Logic Controller with learning capabilities are a specially interesting subject of study, due to its format and characteristics.In order to control systems with multiple inputs and one output it was studied fuzzy control algorithms with an adaptive component, in other words, with the capacity to adapt the existing controller rules and parameters and with an evolving component, in other words, with the capacity to modify the controller structure with the addition of new rules, using the historical data about the controlled system. Furthermore, the control system must be able to change its structure at the same time is controlling the system, don't need to do offline training and also be able to control unknown systems without previous knowledge of the model and dynamics of the systems. After some research, an algorithm with the mentioned characteristics was chosen and served as the basis for the algorithm proposed in this dissertation.In this work are presented the concepts of Fuzzy Controllers, the architecture and structure of the proposed algorithm, being mentioned the improvements to the detected faults of the original algorithm that was studied. The importance and influence of several parameters of the proposed algorithm are also analysed in detail.In order to validate and demonstrate the capacity of the proposed algorithm, it was tested and analysed its performance in the control of several simulated systems with multiple inputs and one output, as well as in a real non-linear system based on two-coupled DC motors. All tested system were also subjected to perturbations, being analysed the response of the proposed algorithm.
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Jiun-Fei, Shiu. "fuzzy sliding-mode control." 1998. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611313631.
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Jiun-Fei, Shiu, and 許駿飛. "fuzzy sliding-mode control." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/20770297998362157818.
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碩士<br>元智大學<br>電機與資訊工程研究所<br>87<br>Fuzzy logic controller is very suitable for multi-input multi-output (MIMO) nonlinear systems with controller which is complex and not easy realized by the classical design method.
Several fuzzy sliding-mode control laws are proposed in this thesis. The fuzzy logic method is applied to reduce the chattering control signal in conventional sliding-mode controller. The problems in PI- or PD-type fuzzy logic controllers: (1) the stability on FLC; (2) the huge number of fuzzy rules, are solved. The proposed fuzzy sliding-mode controller design methods are applied to three practical systems; the pendulum systems, the robotic systems and the flight control systems. Simulation results demonstrate that the system performance is improved sufficiently and the stability and robustness properties are also possessed.
Finally, the adaptive technique is added to fuzzy sliding-mode control to improve the system response by changing the sliding surface.
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Lu, Chi-Chiuan, and 爐啟銓. "Robust Fuzzy Tracking control." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/s3v5tf.
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碩士<br>國立高雄應用科技大學<br>電機工程系博碩士班<br>96<br>This issue of robust fuzzy control of T-S fuzzy systems is studied in the thesis. For the cases of systems with and without disturbance and uncertainties, the state-feedback fuzzy tracking controllers are designed respectively. All the designed conditions are expressed in the form of LMIs, thus they are numerically realizable. From the simulations of examples, it can be seen that the proposed approach achieves a better tracking performance in comparison with existing literature.
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Lee, Ju-Chang, and 李如章. "Intelligent Fuzzy Logic Control." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/90022675221436185319.
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碩士<br>元智大學<br>電機工程研究所<br>88<br>Fuzzy logic controller is very suitable for multi-input multi-output (MIMO) nonlinear systems with controller which is complex and not easy realized by the classical design method.
Several fuzzy sliding-mode control, self-organizing fuzzy sliding-mode control and adaptive fuzzy sliding-mode control are proposed in this thesis. The fuzzy logic method is applied to reduce the chattering control signal in conventional sliding-mode controller. The problems in fuzzy logic controllers: (1) the stability on FLC; (2) the huge number of fuzzy rules, are solved. The proposed fuzzy sliding-mode controller design methods are applied to three practical systems; the servo motor systems and the robotic systems. Simulation results demonstrate that the system performance is improved sufficiently and the stability and robustness properties are also possessed.
Finally, the adaptive technique is added to fuzzy sliding-mode control to improve the system response by changing the sliding surface.
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Jiunn-Lin, Huang, and 黃俊霖. "Fuzzy Model Predictive Control." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/12722884493929113679.
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碩士<br>國立臺灣大學<br>化學工程學研究所<br>87<br>This thesis aims to present the construction of the multi-linear model and
multi-linear model based control system design. The multi-linear model
divides the possible operating regime into several sub-regimes and each
linear model represents the dynamic behavior of the specific sub-regime.
The multi-linear model combines several linear models according to the
corresponding weighting to a global model. The weighting of the linear
model depends on states. In this thesis, the decision of the weighting
magnitude uses the concept of fuzzy logic inference.
For a nonlinear system, dynamic matrix control based on the multi-linear model
is presented in this thesis. One linear controller is designed for each
sub-regime, and the current control output is obtained by the weighting sum
of all sub-controller outputs, parameters or output changes. Finally, the
control strategy is illustrated by two chemical processes. One is the
neutralization process and the other is the mixing tank with temperature and level control.
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Kuo, Pan-Jung, and 郭盼容. "A Fuzzy Gain Tuner for Networked Fuzzy Control system." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/hs5b22.
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碩士<br>國立臺北科技大學<br>機電整合研究所<br>94<br>Aim at the control system that communication by network system, this thesis proposes a “Fuzzy Gain Tuner, FGT” that can deal with the random delay of the network to cause an unstable and even not controllable result. The FGT considering the network delay status by the round-trip time (RTT) of network transmits a round-trip. Utilize RTT to analyze the congestion and the variation of the network delay. In addition, the fuzzy rules of FGT can learning automatically by genetic algorithm (GA) and the Takagi-Sugeno-Kang model (TSK) that considering many network delay models to get the better performance under difference delay conditions. Finally, experiment of path following and obstacle avoidance by the automatic guided vehicle (AGV) will prove the FGT feasibility in the practice control system.
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Yang, Bo-Yun, and 楊博允. "Apply Grey-Fuzzy-Fuzzy Control to the Synchronized and Tension Control on Roll Machines." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/szu7cc.
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碩士<br>義守大學<br>電機工程學系<br>104<br>The roll machine is used to roll the product, such as steel roll and toilet paper. The stable synchronism and the constant tension will decide the quality of the roll result. A novel control scheme is proposed in this paper for the control objective. Each feedback loop is controlled by the Grey-Fuzzy-Fuzzy controller. Two-layer fuzzy control is introduced to achieve precious synchronism and tension control. The Grey prediction is combined to overcome the problem of inertia. Finally, a prototype of roll machine is implemented for examining the proposed control scheme. Experimental results show that the Grey-Fuzzy-Fuzzy control is robust to against the external load. The experimental results also show that the proposed control rule has robustness and practical capability.
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Chuang, Feng-Chih, and 莊峰誌. "Fuzzy Control and Fuzzy Sliding Mode Control Design of a Ship-carried Satellite Antenna." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/94633117475153477055.
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碩士<br>中原大學<br>機械工程研究所<br>90<br>This study is intended to build a scale model of a ship-carried satellite antenna and to design fuzzy control and fuzzy sliding mode control schemes to track the aiming angle of the antenna. The scale model of the antenna is designed with two angular degrees of freedom, azimuth and elevation. To accomplish the closed-loop control, two encorders are installed to rotational axes of azimuth and elevation to provide instant feedback signals for the current aiming angle of the antenna. With the scale model designed and fabricated ready, control schemes are next forged. For Fuzzy control design, membership functions are first determined, and followed by if-then rules and defuzzification. For fuzzy sliding mode control design, sliding surface is first determined, and then the standard procedure of fussy control design is followed to synthesize the controller. The responses of the system are expected to first converge to the sliding surface and then to desired states. Numerical simulations are conducted for validating effectiveness of fuzzy controllers designed with various memberships and sliding surfaces. It shows that the designed controllers are able to perform effective tracking for the ship-carried antenna.
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Yu, Chi-Cheng. "Automatic Exposure with Fuzzy Control." 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2306200414113400.
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Yang, Chen-Ta. "Microprocessor embedded fuzzy control system." 1992. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611304181.
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Hsu, Cheng-Hung, and 許鎮洪. "Robust mixed-norm fuzzy control." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/31230983995866764597.
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Chen, Jen-Yang, and 陳珍源. "Adaptive Fuzzy Control System Design." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/41837319997903382326.
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博士<br>淡江大學<br>電機工程學系<br>88<br>This dissertation focuses on the design of the following two intelligent controllers: fuzzy controller and extension controller. For the fuzzy controller, three parameters including control rules, membership functions, and scaling factor are well distinct adapted using adaptation approach. A new rule analytical form with multi-variables is proposed such that the direct and indirect adaptation approaches can be successfully applied to high order systems. A fuzzy sliding mode controller via rule adaptation is also shown in the contents. Such structure of fuzzy controller is usually easier than that of using state variables, because the fuzzy sliding mode controller contains less input variables. Regarding to the membership functions adaptation, only one parameter is used to adapt the membership functions. After adaptation, the membership functions are always equally distributed over the universe of discourse. Here, we focus on the design of direct and indirect fuzzy sliding mode controllers. For the scaling factor adaptation, two popular types, Mamdani and Takagi-Sugeno fuzzy controllers, are investigated. Of course, the best advantage of scaling factor is that the remaining parameters of fuzzy controller including rules and membership functions do not need to change under control, we can determine it under control common sense. Combination of the Mamdani and the Takagi-Sugeno models in designing the indirect fuzzy control via scaling factor adaptation is also studied. Two robust control terms, bang-bang control and hitting control, are illustrated in order to guarantee the stability of developed fuzzy control systems. For the extension controller, based on the scheme of adaptive and sliding mode control, two distinct extension controllers are investigated. Some simulation works of nonlinear control system for the developed controllers are given. As to what we expect, the simulation results show the effectiveness of the developed controllers.
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Yu, Chi-Cheng, and 游啟昌. "Automatic Exposure with Fuzzy Control." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/62870538058009822856.
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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>92<br>In this thesis, we will present an automatic exposure method based on Nikon’s AMP (Automatic-Multiple-Pattern). Our method will add the fuzzy control into the AMP to get smooth transition effect.
AMP needs to collect a lot of information for its database to offer optimal exposures for various conditions. In other words, the database is very huge and needs to cover all conditions, include weather conditions, contrast conditions, subject condition (subject is bright or dark) and so forth. We use fuzzy control to simulate the various conditions. It means we only construct three main frame reference tables, fuzzy control will simulate the ambiguous conditions (conditions not included in the reference table) to get a smooth transition effect. In this thesis, we also implement a subject growing function for dynamically guess where subject is. Our method classifies subject and background to get a better exposure. We also implement an easy evaluative function to estimate what is a well exposed picture in the same scene with different exposure parameters. The experiment results show that our evaluative function selects the best exposed picture similar to most people’s selection.
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Weng, Chiu-Chan, and 翁久展. "Fuzzy Control of Mobile Robots." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/92750847151274330470.
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碩士<br>中華大學<br>電機工程學系碩士班<br>91<br>It is difficult to deal with the uncertainty of system and external disturbance. Minimax tracking design for mobile cars based on the adaptive fuzzy elimination scheme is implemented. There are two principal characteristics in this study. First is to eliminate the uncertainty of systems by using the adaptive fuzzy. And second is to reduce the influence of error movement systems caused by disturbance to be minimum by using the minimax technique in game theory.
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Huang, Bing-Chyi, and 黃柄圻. "Adaptive Fuzzy Control System Design." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/26985233531754948858.
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碩士<br>淡江大學<br>電機工程學系<br>85<br>To control various nonlinear systems, the fazzy controller is the most popular metho recently. However, the designs of fuzzy systems depend on knowledge acquisition by experience of domain experts and adjust rule base and membership function by design engineers. The method will spend much time and the performance of the controlled system can not be guaranteed to achieve optimal result. Therefore, we propose direct adaptive fuzzy control system and indirect adaptive fuzzy control system to improve the disadvantages of traditional fuzzy system. Simply speaking, the designed method of direct adaptive fuzzy control system generates a control command from the single fuzzy controller, then feed it back the plant. The designed method of indirect adaptive fuzzy control system is modeling the plant from a number of fuzzy systems at first, then it generates a control command by the dynamic character which will feed it back the plant. Whatever which types of adaptive fuzzy controller, they have the adaptive law of center regulation or rule regulation on line, respectively. The adaptive law not only can overcome the defect of genetic algorithms and simulated annealing which are unable to find the optimal membership function and rule base on line, but also the results of the controlled system will not be influenced, even the plant has many uncertainties, such as the suddenly change of parameters or the external disturbance. Further, we use a few parameter to design the adaptive law in order to reduce the complex of mathematical deduction and increase the speed of computing. Therefore, the fuzzy system becomes more practical and robust.
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Zheng, Da-Xin, and 鄭達新. "Adaptive fuzzy sliding mode control." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/29633408501579642918.
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碩士<br>國立中央大學<br>機械工程學系<br>85<br>A decoupled adaptive fuzzy sliding mode controller (DAFSMC) is
proposed in this paper. The motivation behind this is to
incorporate the best features of self-tuning fuzzy control and
sliding-mode control with decoupled method to control an
unstable 4-th order inverted pendulum system. The consequent
parameters of the membership functions in the fuzzy rule base
are tuned according to some adaptive algorithm so as to control
the states of two subsystems, decoupled from the 4-th order
system, to hit each user-defined sliding surface and then slide
along it continuously. The initial IF-THEN rules in the DAFSMC
can be randomly selected or roughly given by human experts, and
then automatically tuned by a direct adaptive law, therefore,
reducing the expertise dependency in the design procedure of
fuzzy logic control. By applying the DAFSMC to control a
nonlinear unstable inverted pendulum system, the simulation
results show that the expected approximation sliding property
was occurred, and the dynamic behavior of control system can be
determined by the sliding surface. Finally, adaptive fuzzy
sliding mode control is applied to control a 6-th order
nonlinear system and confirms the validity of the proposed
approach.
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Liao, Yong-chih, and 廖勇智. "Fuzzy Control Theorem Applied in Structural Strain Control." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/35436268264504719571.
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碩士<br>國立臺灣科技大學<br>機械工程系<br>89<br>In general, the external disturbance will always cause the structural deflection and unexpected vibration problem. The concepts of passive and active vibration control are two typical approaches to depress the disturbance effect. In this thesis, the concept of active control will be proposed for the structural strain control. The control algorithm will use the self-organizing fuzzy control theorem . To associate with PC and DSP, an electrohydraulic servo control system will adopt as the active force generator. Finally, the experiments will be implemented to evaluate the control performance of various disturbances and the feasibility of proposed control algorithm.
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Yang, Dong Lung, and 楊棟樑. "Adaptive fuzzy control based on natural control laws." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/25907345831636943581.
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碩士<br>國立成功大學<br>機械工程研究所<br>81<br>Fuzzy controllers based on linguistic rules are in general
nonlinear. Mathematical analysis of the systems with such
controller is often difficult and not much work has been
reported.It seems that a major criticism against fuzzy control
is the lack of mathematical analysis. Structural parameters of
a fuzzy controller are designed by either trial and error or by
expert's knowledge and experience. So, at present there is no
systematic procedure for the design of fuzzy controller. In
this work the natural control laws and the regular fuzzy set
are employed and the design problem is reduced to two pre-gain
parameters i.e., gain parameter of error and gain parameter of
error change, and a post-gain parameter of control output for a
PD-type fuzzy controller. The control output of natural control
laws fuzzy controller is the weighting mean between a linear
state feedback control term and a quantizing state control
term. The weighting factors are dependent on error and error
change. The set of three gains is determined by two equtions
derived from linear state feedback control and another eqution
which is obtained from expert's knowledge. Rule self-
regulating fuzzy controller has the same property. A systematic
procedure for designing the natural control laws fuzzy
controller or rule self-regulating fuzzy controller is
suggested in this thesis for the first time. The controllers
are implemented for the tracking control of a DC servomotor and
the vibration control of a single-link flexible arm. The
results indicate that the natural control law fuzzy controller
has a better performance in that smaller tracking error
variance and less amount of control effort than the linear
state feedback controller are achieved. In particular, the rule
self-regulating fuzzy controller has the best performance.