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1
Joergensen, Dorte Rich. "Automated commissioning of building control systems." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244525.
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Daneshpooy, Alireza. "Artificial neural network and fuzzy logic control for HVDC systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23593.pdf.
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Lin, Yuetong. "MODULAR CONSTRUCTION OF FUZZY LOGIC CONTROL SYSTEMS." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/193845.
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This dissertation presents a novel approach to combining wavelet networks and multi-layer feedforward network for fuzzy logic control systems. Most of the existing methods focus on implementing the Takagi-Sugano fuzzy reasoning model and have demonstrated its effectiveness. However, these methods fail to keep the knowledge structure, which is critical in interpreting the learning process and providing insights to the working mechanism of the underlying systems. It is our intention here to continue the previous research by the PARCS group in this area by utilizing individual subnets to implement decision-making process with the fuzzy logic control systems based on the Mamdani model. Center Average defuzzification has seen its implementation by a neural network so that a succinct network structure is obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time. Offline orthogonal least squares method is used for training the wavelet subnets and the overall systems is updated using the steepest descent algorithm. Simulation results have shown the efficacy of this new approach in applications including system modeling and time series prediction.
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Mathur, Garima. "Fuzzy logic control for infant-incubator systems." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153768682.
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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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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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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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El-Deen, M. M. G. Naser. "Adaptive fuzzy logic control for solar buildings." Thesis, Northumbria University, 2002. http://nrl.northumbria.ac.uk/2084/.
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Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adaptive control method for space heating in buildings with significant solar input. A simulation model of a building space to assess the performance of different control strategies is developed. A lumped parameter model based on an optimisation technique has been proposed and validated. It is shown that this model gives an improvement over existing low order modelling methods. A detailed model of a hot water heating system and related control devices is developed and evaluated for the specific purpose of control simulation. A PI-based fuzzy logic controller is developed in which the error and change of error between the internal air temperature and the user set point temperature is used as the controller input. A conventional PD controller is also considered for comparison. The parameters of the controllers are set to values that result in the best performance under likely disturbances and changes in setpoint. In a further development of the fuzzy logic controller, the Predicted Mean Vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. The controller then adjusts the air temperature set point in order to satisfy the required comfort level given the prevailing values of other comfort variables contributing to the comfort sensation. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. The need to develop an adaptive capability in the fuzzy logic controller to account for lagging influence of solar heat gain is established and a new adaptive controller has therefore been proposed. The development of a "quasi-adaptive" fuzzy logic controller is developed in two steps. A feedforward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then modified to have two inputs: the first input being the error between the setpoint temperature and the internal air temperature and the second the predicted future internal air temperature. When compared with a conventional method of control the proposed controller is shown to give good tracking of the setpoint temperature, reduced energy consumption and improved thermal comfort for the occupants by reducing solar overheating. The proposed controller is tested in real time using a test cell equipped with an oil- filled electric radiator, temperature and solar sensors. Experimental results confirm earlier findings arrived at by simulations, in that the proposed controller achieves superior tracking and reduces afternoon solar overheating, when compared with a conventional method of control.
9
Baxter, Jeremy. "Fuzzy logic control of an automated guided vehicle." Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5817/.
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This thesis describes the fuzzy logic based control system for an automated guided vehicle ( AGV ) designed to navigate from one position and orientation to another while avoiding obstacles. A vehicle with an onboard computer system and a beacon based location system has been used to provide experimental confirmation of the methods proposed during this research. A simulation package has been written and used to test control techniques designed for the vehicle. A series of navigation rules based upon the vehicle's current position relative to its goal produce a fuzzy fit vector, the entries in which represent the relative importance of sets defined over all the possible output steering angles. This fuzzy fit vector is operated on by a new technique called rule spreading which ensures that all possible outputs have some activation. An obstacle avoidance controller operates from information about obstacles near to the vehicle. A method has been devised for generating obstacle avoidance sets depending on the size, shape and steering mechanism of a vehicle to enable their definition to accurately reflect the geometry and dynamic performance of the vehicle. Using a set of inhibitive rules the obstacle avoidance system compiles a mask vector which indicates the potential for a collision if each one of the possible output sets is chosen. The fuzzy fit vector is multiplied with the mask vector to produce a combined fit vector representing the relative importance of the output sets considering the demands of both navigation and obstacle avoidance. This is operated on by a newly developed windowing technique which prevents any conflicts produced by this combination leading to an undesirable output. The final fit vector is then defuzzified to give a demand steering angle for the vehicle. A separate fuzzy controller produces a demand velocity. In tests carried out in simulation and on the research vehicle it has been shown that the control system provides a successful guidance and obstacle avoidance scheme for an automated vehicle.
10
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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Antão, Rómulo José Magalhães Martins. "Type-2 fuzzy logic: uncertain systems' modeling and control." Doctoral thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18041.
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Doutoramento em Engenharia Eletrotécnica<br>A última fronteira da Inteligência Artificial será o desenvolvimento de
um sistema computacional autónomo capaz de "rivalizar" com a capacidade
de aprendizagem e de entendimento humana. Ainda que tal
objetivo não tenha sido até hoje atingido, da sua demanda resultam
importantes contribuições para o estado-da-arte tecnológico atual. A
Lógica Difusa é uma delas que, influenciada pelos princípios fundamentais
da lógica proposicional do raciocínio humano, está na base
de alguns dos sistemas computacionais "inteligentes" mais usados da
atualidade.
A teoria da Lógica Difusa é uma ferramenta fundamental na suplantação
de algumas das limitações inerentes à representação de informação
incerta em sistemas computacionais. No entanto esta apresenta
ainda algumas lacunas, pelo que diversos melhoramentos à teoria
original têm sido introduzidos ao longo dos anos, sendo a Lógica
Difusa de Tipo-2 uma das mais recentes propostas. Os novos graus de
liberdade introduzidos por esta teoria têm-se demonstrado vantajosos,
particularmente em aplicações de modelação de sistemas não-lineares
complexos. Uma das principais vantagens prende-se com o aumento
da robustez dos modelos assim desenvolvidos comparativamente àqueles
baseados nos princípios da Lógica Difusa de Tipo-1 sem implicar
necessariamente um aumento da sua dimensão. Tal propriedade é particularmente
vantajosa considerando que muitas vezes estes modelos
são utilizados como suporte ao desenvolvimento de sistemas de controlo
que deverão ser capazes de assegurar o comportamento ótimo
de um processo em condições de operação variáveis. No entanto, o
estado-da-arte da teoria de controlo de sistemas baseada em modelos
não tem integrado todos os melhoramentos proporcionados pelo desenvolvimento
de modelos baseados nos princípios da Lógica Difusa de
Tipo-2.
Por essa razão, a presente tese propõe-se a abordar este tópico desenvolvendo
uma metodologia de síntese de Controladores Preditivos
baseados em modelos Takagi-Sugeno seguindo os princípios da Lógica
Difusa de Tipo-2. De modo a cumprir este objetivo, quatro linhas de
investigação serão debatidas neste trabalho.Primeiramente proceder-se-á ao desenvolvimento de uma metodologia
de treino de Modelos Difusos de Tipo-2 simplificada, focada em dois
paradigmas: manter a clareza dos intervalos de incerteza introduzidos
sobre um Modelo Difuso de Tipo-1; assegurar a validade dos diversos
modelos localmente lineares que constituem a estrutura Takagi-
Sugeno, de modo a torná-los adequados a métodos de síntese de controladores
baseados em modelos.
O modelo desenvolvido é tipicamente utilizado para extrapolar o comportamento
do sistema numa janela temporal futura. No entanto,
quando usados em aproximações de sistemas não lineares, os modelos
do tipo Takagi-Sugeno estabelecem um compromisso entre exatidão e
complexidade computacional. Assim, é proposta a utilização dos princípios
da Lógica Difusa de Tipo-2 para reduzir a influência dos erros de
modelação nas estimações obtidas através do ajuste dos intervalos de
incerteza dos parâmetros do modelo.
Com base na estrutura Takagi-Sugeno, um método de linearização local
de modelos não-lineares será utilizado em cada ponto de funcionamento
do sistema de modo a obter os parâmetros necessários para a
síntese de um controlador otimizado numa janela temporal futura de
acordo com os princípios da teoria de Controlo Preditivo Generalizado -
um dos algoritmos de Controlo Preditivo mais utilizado na indústria. A
qualidade da resposta do sistema em malha fechada e a sua robustez a
perturbações serão então comparadas com implementações do mesmo
algoritmo baseadas em métodos de modelação mais simples.
Para concluir, o controlador proposto será implementado num
System-on-Chip baseado no core ARM Cortex-M4. Com o propósito
de facilitar a realização de testes de implementação de algoritmos
de controlo em sistemas embutidos, será apresentada também uma
plataforma baseada numa arquitetura Processor-In-the-Loop, que permitirá
avaliar a execução do algoritmo proposto em sistemas computacionais
com recursos limitados, aferindo a existência de possíveis
limitações antes da sua aplicação em cenários reais.
A validade do novo método proposto é avaliada em dois cenários de
simulação comummente utilizados em testes de sistemas de controlo
não-lineares: no Controlo da Temperatura de uma Cuba de Fermentação
e no Controlo do Nível de Líquidos num Sistema de Tanques
Acoplados. É demonstrado que o algoritmo de controlo desenvolvido
permite uma melhoria da performance dos processos supramencionados,
particularmente em casos de mudança rápida dos regimes de funcionamento
e na presença de perturbações ao processo não medidas.<br>The development of an autonomous system capable of matching
human knowledge and learning capabilities embedded in a compact
yet transparent way has been one of the most sought milestones of
Artificial Intelligence since the invention of the first mechanical general
purpose computers. Such accomplishment is yet to come but, in its
pursuit, important contributions to the state-of-the-art of current technology
have been made. Fuzzy Logic is one of such, supporting some
of the most used frameworks for embedding human-like knowledge in
computational systems.
The theory of Fuzzy Logic overcame some of the difficulties that the
inherent uncertainty in information representations poses to the development
of computational systems. However, it does present some
limitations so, aiming to further extend its capabilities, several improvements
over its original formalization have been proposed over the
years such as Type-2 Fuzzy Logic - one of its most recent advances.
The additional degrees of freedom of Type-2 Fuzzy Logic are showing
greater potential to supplant its original counterpart, especially in
complex non-linear modeling tasks. One of its main outcomes is its
capability of improving the developed model’s robustness without necessarily
increasing its dimensionality comparatively to a Type-1 Fuzzy
Model counterpart. Such feature is particularly advantageous if one
considers these model as a support for developing control systems capable
of maintaining a process’s optimal performance over changing
operating conditions. However, state-of-the art model-based control
theory does not seem to be taking full advantage of the improvements
achieved with the development of Type-2 Fuzzy Logic based models.
Therefore, this thesis proposes to address this problem by developing a
Model Predictive Control system supported by Interval Type-2 Takagi-
Sugeno Fuzzy Models. To accomplish this goal, four main research
directions are covered in this work.Firstly, a simpler method for training a Type-2 Takagi-Sugeno Fuzzy
Model focused on two main paradigms is proposed: maintaining a
meaningful interpretation of the uncertainty intervals embedded over
an estimated Type-1 Fuzzy Model; ensuring the validity of several locally
linear models that constitute the Takagi-Sugeno structure in order
to make them suitable for model-based control approaches.
Based on the developed model, a multi-step ahead estimation of the
process behavior is extrapolated. However, as Takagi-Sugeno Fuzzy
Models establish a trade-off between accuracy and computational complexity
when used as a non-linear process approximation, it is proposed
to apply the principles of Type-2 Fuzzy Logic to reduce the influence
of modeling uncertainties on the obtained estimations by adjusting the
model parameters’ uncertainty intervals.
Supported by the developed Type-2 Takagi-Sugeno Fuzzy Model, a
locally linear approximation of each current operation point is used to
obtain the optimal control law over a prediction horizon according to
the principles of Generalized Predictive Control - one of the most used
Model Predictive Control algorithms in Industry. The improvements in
terms of closed loop tracking performance and robustness to unmodeled
operation conditions are then assessed comparatively to Generalized
Predictive Control implementations based on simpler modeling
approaches.
Ultimately, the proposed control system is implemented in a general
purpose System-on-a-Chip based on a ARM Cortex-M4 core. A
Processor-In-the-Loop testing framework, developed to support the implementation
of control loops in embedded systems, is used to evaluate
the algorithm’s turnaround time when executed in such computationally
constrained platform, assessing its possible limitations before deployment
in real application scenarios.
The applicability of the new methods introduced in this thesis is illustrated
in two simulated processes commonly used in non-linear control
benchmarking: the Temperature Control of a Fermentation Reactor
and the Liquid Level Control of a Coupled Tanks System. It is shown
that the developed control system achieves an improved closed loop
performance of the above mentioned processes, particularly in the cases
of quick changes in the operation regime and in presence of unmeasured
external disturbances.
12
Peters, Barry. "Stable fuzzy logic controllers for uncertain dynamic systems." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/18223.
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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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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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Breedon, Philip James. "Multiple axis fuzzy logic control of an industrial robot." Thesis, Nottingham Trent University, 2001. http://irep.ntu.ac.uk/id/eprint/10298/.
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Robot control systems can be considered complex systems, the design of a controller involving the determination of the dynamic model for the system. This in itself can be a complicated task due to non-linearities, multiple axis (degrees of freedom) control and the constantly changing working environment. Problems arise when the theoretical model produced for such a system is not accurate. When developing a controller using conventional techniques a design scheme has to be produced, usually based on a model of the system. In addition kinematics equations must be derived to take into account the physical boundaries of the system. The work outlined in this thesis utilises fuzzy logic control to address these control issues. Fuzzy logic provides functional capability without the use of a system model and has characteristics suitable for capturing the approximate, vaiying values found in real world systems. Initial development of a single axis fuzzy logic control system was implemented on a Dainichi industrial five-axis robot, replacing the existing control and hardware systems with a new developmental system. The concept of fuzzy logic and its application to control highlights the potential advantages that fuzzy logic control (PLC) can provide when compared to the more conventional control methodologies. Additional new control hardware has been interfaced to an existing robot manipulator, making it possible to compare PLC and PIDVF (Proportional Integral Derivative Velocity FeedforwardlFeedback) controllers for single axis development. Average response time and overshoot for a given set point were compared for each system. The results proved that, using a basic PLC minimal overshoot and fast rise times could be achieved in comparison to the commercial PIDVF system. Further research concentrated on the development of the control software to provide multiple axis control for an industrial robot using a continuous path algorithm. The more from single axis to multiple axis control provided a much more complex control problem. A novel and innovative process for the fuzzy controller was implemented with up to three axes reaching the target point simultaneously. Control of the industrial robot was investigated using methods that were more suited to real time controL The most significant change was a reduction in the number of fuzzy rules when compared to single axis control. During robot control no adaptation of the rule base or membership functions was carried Out Ofl line; only system gain was modified in relation to link speed and joint error within predetermined design parameters. The fuzzy control system had to manage the effects of frictional and gravitational forces whilst compensating for the varying inertia components when each linkage is moving. Testing based on ISO 9283 for path accuracy and repeatability verified that real time control of three axes was achievable with values of 938tm and 864tm recorded for accuracy and repeatability respectively. The development of novel industrial robot real time multi-axis fuzzy controller has combined new control hardware with an efficient fuzzy engine addressing inverse kinematics, scaling and dynamic forces in order to provide a viable robot control system.
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Houchin, Scott J. "Pendulum : controlling an inverted pendulum using fuzzy logic /." Online version of thesis, 1991. http://hdl.handle.net/1850/11294.
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Lee, James X. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq26881.pdf.
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Lee, James X. (James Xiang) Carleton University Dissertation Engineering Mechanical and Aerospace. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Ottawa, 1997.
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Polkinghorne, Martyn Neal. "A self-organising fuzzy logic autopilot for small vessels." Thesis, University of Plymouth, 1994. http://hdl.handle.net/10026.1/1100.
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Currently small vessels use autopilots based on the Proportional plus Integral plus Derivative (PID) algorithm which utilises fixed gain values. This type of autopilot is known to often cause performance difficulties, a survey is therefore carried out to identify the alternative autopilot methods that have been previously investigated. It is shown that to date, all published work in this area has been based on large ships, however, there are specific difficulties applicable to the small vessel which have therefore not been considered. After the recognition of artificial neural networks and fuzzy logic as being the two most suitable techniques for use in the development of a new, and adaptive, small vessel autopilot design, the basic concepts of both are reviewed and fiizzy logic identified as being the most suitable for this application. The remainder of the work herein is concerned with the development of a fuzzy logic controller capable of a high level of performance in the two modes of coursekeeping and course-changing. Both modes are integrated together by the use of nonlinear fuzzy input windows. Improved performance is then obtained by using a nonlinear fuzzy rulebase. Integral action is included by converting the fuzzy output window to an unorthodox design described by two hundred and one fuzzy singletons, and then by shifting the identified fuzzy sets to positive, or negative, in order that any steady-state error may be removed from the vessel's performance. This design generated significant performance advantages when compared to the conventional PID autopilot. To develop further into an adaptive form of autopilot called the self-organising controller, the single rulebase was replaced by two enhancement matrices. These are novel features which are modified on-line by two corresponding performance indices. The magnitude of the learning was related to the observed performance of the vessel when expressed in terms of its heading error and rate of change of heading error. The autopilot design is validated using both simulation, and full scale sea trials. From these tests it is demonstrated that when compared to the conventional PID controller, the self-organising controller significantly improved performance for both course-changing and course-keeping modes of operation. In addition, it has the capability to learn on-line and therefore to maintain performance when subjected to vessel dynamic or environmental disturbance alterations.
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Jin, Gang-Gyoo. "Intelligent fuzzy logic control of processes with time delays." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388058.
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Stockton, Nicklas O. "Hybrid Genetic Fuzzy Systems for Control of Dynamic Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635312922039.
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Watanabe, Yukio. "Learning control of automotive active suspension systems." Thesis, Cranfield University, 1997. http://dspace.lib.cranfield.ac.uk/handle/1826/13865.
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This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.
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張大任 and Tai-yam Cheung. "Evolutionary design of fuzzy-logic controllers for overhead cranes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31243010.
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Cheung, Tai-yam. "Evolutionary design of fuzzy-logic controllers for overhead cranes /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23636543.
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Raad, Raad. "Neuro-fuzzy admission control in mobile communications systems." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061030.153500/index.html.
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Kirawanich, Phumin. "Fuzzy logic control for an active power line conditioner /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060114.
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Xiao, Bo. "Stability and performance analysis of polynomial fuzzy-model-based control systems and interval type-2 fuzzy logic systems." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/stability-and-performance-analysis-of-polynomial-fuzzymodelbased-control-systems-and-interval-type2-fuzzy-logic-systems(1a455ca8-f27d-49aa-ab4a-8ae697aeba17).html.
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The main research objective in this thesis is to investigate the stability and performance of the interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) control system. PFMB control scheme has been developed recently around 2009 and demonstrates more potential than the traditional Takagi-Sugeno fuzzy-model-based (T-S FMB) control approach to represent the nonlinearities in the plant. Meanwhile, the IT2 fuzzy logic has also been proposed to incorporate uncertainties of the nonlinear systems into the membership functions directly. Through the IT2 PFMB control design approach, both the nonlinearity and the uncertainty in the system can be handled well. The control performance and the relaxation of stability conditions of IT2 PFMB control systems are studied and investigated in the thesis. The main contribution of the thesis is summarized in three tasks and presented as following: In the first task in Chapter 3, the stability conditions of the PFMB systems equipped with mismatched IT2 membership functions are investigated. Unlike the membership-function-independent (MFI) methods, the information and properties of IT2 membership functions are considered in the stability analysis and contained in the stability conditions in terms of sum-of-squares (SOS) based on the Lyapunov stability theory. Three methods, demonstrating their own merits, are proposed to conduct the stability analysis for the IT2 PFMB control systems and all of the methods can achieve feasible control results. All the three approaches are well explained, which offers the reader systematic ways to include the information of the membership functions into the analysis. In addition, all the approaches are compared and the pros and cons are presented to help the reader choose the most appropriate approach in the applications. In the second task presented in Chapter 4, the membership-functions-dependent (MFD) methods have been proceeded to the tracking control problems and the output feedback tracking issues of IT2 PFMB fuzzy control systems are investigated. The output-feedback IT2 polynomial fuzzy controller connected with the nonlinear plant in a closed loop drives the system states of the nonlinear plant to track those of the stable reference model. The system stability is investigated based on the Lyapunov stability theory under the SOS-based analysis approach and the SOSbased stability conditions are derived subject to a prescribed H1 performance. Like in the first work, the information of membership functions is also included in the analysis to facilitate the analysis and help improve the tracking performance in terms of H1 performance. Considering the implementation of the mentioned control schemes on digital computers, the sampled-data control systems are investigated as the last work in the thesis, which is presented in Chapter 5. In this task, the IT2 PFMB tracking control system is extended to the sampled-data based one. Through using the sampled output of both the control system and the reference system, an IT2 polynomial sampled-data based output feedback fuzzy controller can be designed to ful ll the tracking control task, the stability conditions can be obtained in terms of SOS and the tracking error is attenuated by the H1 performance index. As did in the previous two works, the information of the IT2 membership functions is used to relax the stability conditions and improve the tracking performance. The approaches proposed in the thesis to relax the stability conditions as well as to improve the tracking performance of the IT2 PFMB control systems are proved through the Lyapunov based stability theory. Meanwhile, simulation examples are provided to demonstrate and verify the theoretical analysis.
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FarinWata, Shehu Saíd. "Performance assessment of fuzzy logic control systems via stability and robustness measures." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/14797.
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Majara, Khotso Ernest. "A fuzzy logic control system for a friction stir welding process." Thesis, Nelson Mandela Metropolitan University, 2006. http://hdl.handle.net/10948/405.
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FSW is a welding technique invented and patented by The Welding Institute in 1991. This welding technique utilises the benefits of solid-state welding to materials regarded as difficult to weld by fusion processes. The productivity of the process was not optimised as the real-time dynamics of the material and tool changes were not considered. Furthermore, the process has a plastic weld region where no traditional modelling describing the interaction between the tool and work piece is available. Fuzzy logic technology is one of the artificial intelligent strategies used to improve the control of the dynamics of industrial processes. Fuzzy control was proposed as a viable solution to improve the productivity of the FSW process. The simulations indicated that FLC can use feed rate and welding speed to adaptively regulate the feed force and tool temperature respectively, irrespective of varying tool and material change. The simulations presented fuzzy logic technology to be robust enough to regulate FSW process in the absence of accurate mathematical models.
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Kong, Kou A. "Fuzzy logic PD control of a non-linear inverted flexible pendulum." [Chico, Calif. : California State University, Chico], 2009. http://hdl.handle.net/10211.4/90.
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Zhou, Jun. "Robust and fuzzy logic approaches to the control of uncertain systems with applications." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1173757489.
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Geng, Guang. "Modelling and control of some nonlinear processes in air-handling systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386699.
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Wijayasekara, Dumidu S. "IMPROVING UNDERSTANDABILITY AND UNCERTAINTY MODELING OF DATA USING FUZZY LOGIC SYSTEMS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4126.
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The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty.
Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty.
However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems.
In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage.
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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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Chwee, Ng Kim. "Switching control systems and their design via genetic algorithms." Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361268.
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Ghwanmeh, Sameh Hussein. "The investigation of on-line self-learning fuzzy logic control for non-linear processes." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337808.
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Bridger, Lee. "Improved control of fed-batch fermenters." Thesis, University of Exeter, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288001.
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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.
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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.
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Liut, Daniel Armando. "Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/29163.
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The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations.
The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained.
The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network.
As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO).<br>Ph. D.
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Wirba, Elias Njoka. "An object-oriented knowledge-based systems approach to construction project control." Thesis, London South Bank University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336366.
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Birkin, Philip. "A novel dual surface type-2 fuzzy logic controller for a micro robot." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12544/.
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Over the last few years there has been an increasing interest in the area of type-2 fuzzy logic sets and systems in academic and industrial circles. Within robotic research the majority of type-2 fuzzy logic investigations has been centred on large autonomous mobile robots, where resource availability (memory and computing power) is not an issue. These large robots usually have a variation of a Unix operating system on board. This allows the implementation of complex fuzzy logic systems to control the motors. Specifically the implementation of interval and geometric type-2 fuzzy logic controllers is of interest as they are shown to outperform type-1 fuzzy logic controllers in uncertain environments. However when it comes to using micro robots it is not practical to use type-1 and type-2 fuzzy logic controllers, due to the lack of memory and the processor time needed to calculate a control output value. The choice of motor controller is usually either fixed pre-set values, a variable scaled value or a PID controller to generate wheel velocities. In this research novel ways of implementing type-1 and interval type-2 fuzzy logic controllers on micro robots with limited resources are investigated. The solution thatis being proposed is the use of pre-calculated 3D surfaces generated by an off-line Fuzzy Logic System covering the expected ranges of the input and output variables. The surfaces are then loaded into the memory of the micro robots and can be accessed by the motor controller. The aim of the research is to test if there is an advantage of using type-2 fuzzy logic controllers implemented as surfaces over type-1 and PID controllers on a micro robot with limited resources. Control surfaces were generated for both type-1 and average interval type-2 fuzzy logic controllers. Each control surface was then accessed using bilinear interpolation to provide the crisp output value that was used to control the motor. Previously when this method has been used a single surface was employed to hold the information. This thesis presents the novel approach of the dual surface type-2 fuzzy logic controller on micro robots. The lower and upper values that are averaged for the classic interval type-2 controller are generated as surfaces and installed on the micro robots. The advantage is that nuances and features of both the lower and upper surfaces are available to be exploited, rather than being lost due to the averaging process. Having conducted the experiments it is concluded that the best approach to controlling micro robots is to use fuzzy logic controllers over the classical PID controllers where ever possible. When fuzzy controllers are used then type-2 fuzzy controllers (dual or single surface) should be used over type-1 fuzzy controllers when applied as surfaces on micro robots. When a type-2 fuzzy controller is used then the novel dual surface type-2 fuzzy logic controller should be used over the classic average surface. The novel dual surface controller offers a dynamic, weighted, adaptive and superior response over all the other fuzzy controllers examined.
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Moon, Myung Soo. "Rule-Based Approaches for Controlling on Mode Dynamic Systems." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30684.
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This dissertation presents new fuzzy logic techniques for designing control systems for a wide class of complex systems. The methods are developed in detail for a crane system which contains one rigid-body and one oscillation mode. The crane problem is to transfer the rigid body a given distance such that the pendulation of the oscillation mode is regulated at the final time using a single control input. The investigations include in-depth studies of the time-optimal crane control problem as an integral part of the work. The main contributions of this study are:
(1) Development of rule-based systems (both fuzzy and crisp) for the design of optimal controllers. This development involves control variable parametrization, rule derivation with parameter perturbation methods, and the design of rule based controllers, which can be combined with model-based feedback control methods.
(2) A thorough investigation and analysis of the solutions for time-optimal control problems of oscillation mode systems, with particular emphasis on the use of phase-plane interpretation.
(3) Development of fuzzy logic control system methodology using expert rules obtained through energy reducing considerations. In addition, dual mode control is a "spin-off" design method which, although no longer time optimal, can be viewed as a near-optimal control method which may be easier to implement. In both types of design optimization of the fuzzy logic controller can be used to improve performance.<br>Ph. D.
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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.
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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.
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Sekercioglu, Ahmet, and ahmet@hyperion ctie monash edu au. "Fuzzy logic control techniques and structures for Asynchronous Transfer Mode (ATM) based multimedia networks." Swinburne University of Technology, 1999. http://adt.lib.swin.edu.au./public/adt-VSWT20050411.130014.
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The research presented in this thesis aims to demonstrate that fuzzy logic is a useful tool for developing mechanisms for controlling traffc flow in ATM based multimedia networks to maintain quality of service (QoS) requirements and maximize resource utilization. The study first proposes a hierarchical, multilevel control
structure for ATM networks to exploit the reported strengths of fuzzy logic at various
control levels. Then, an extensive development and evaluation is presented for a subset of the proposed control architecture at the congestion control level.
An ATM based multimedia network must have quite sophisticated traffc control capabilities to effectively handle the requirements of a dynamically varying mixture of voice, video and data services while meeting the required levels of performance.
Feedback control techniques have an essential role for the effective and efficient management of the resources of ATM networks. However, development of conventional feedback control techniques relies on the availability of analytical system models. The characteristics of ATM networks and the complexity of service
requirements cause the analytical modeling to be very difficult, if not impossible.
The lack of realistic dynamic explicit models leads to substantial problems in developing
control solutions for B-ISDN networks. This limits the ability of conventional techniques to directly address the control objectives for ATM networks. In the literature, several connection admission and congestion control methods for B-ISDN networks have been reported, and these have achieved mixed success. Usually they either assume heavily simplified models, or they are too complicated to implement, mainly derived using probabilistic (steady-state) models.
Fuzzy logic controllers, on the other hand, have been applied successfully to the task of controlling systems for which analytical models are not easily obtainable.
Fuzzy logic control is a knowledge-based control strategy that can be utilized when an explicit model of a system is not available or, the model itself, if available, is highly complex and nonlinear. In this case, the problem of control system design is based on qualitative and/or empirically acquired knowledge regarding the operation of the system. Representation of qualitative or empirically acquired knowledge in a fuzzy logic controller is achieved by linguistic expressions in the form of fuzzy relational equations. By using fuzzy relational equations, classifications related to system parameters can be derived without explicit description.
The thesis presents a new predictive congestion control scheme, Fuzzy Explicit Rate Marking (FERM), which aims to avoid congestion, and by doing so minimize the cell losses, attain high server utilization, and maintain the fair use of links.
The performance of the FERM scheme is extremely competitive with that of control schemes developed using traditional methods over a considerable period of time.
The results of the study demonstrate that fuzzy logic control is a highly effective design tool for this type of problems, relative to the traditional methods. When controlled systems are highly nonlinear and complex, it keeps the human insight alive and accessible at the lower levels of the control hierarchy, and so higher levels
can be built on this understanding.
Additionally, the FERM scheme has been extended
to adaptively tune (A-FERM) so that continuous automatic tuning of the parameters can be achieved, and thus be more adaptive to system changes leading to better utilization of network bandwidth. This achieves a level of robustness that is not exhibited by other congestion control schemes reported in the literature.
In this work, the focus is on ATM networks rather than IP based networks. For historical reasons, and due to fundamental philosophical differences in the (earlier) approach to congestion control, the research for control of TCP/IP and ATM based networks proceeded separately. However, some convergence between them has recently become evident. In the TCP/IP literature proposals have appeared on active queue management in routers, and Explicit Congestion Notication (ECN) for IP. It is reasonably expected that, the algorithms developed in this study will be applicable to IP based multimedia networks as well.
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Farrall, Simon. "A study in the use of fuzzy logic in the management of an automotive heat engine/electric hybrid vehicle powertrain." Thesis, University of Warwick, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387380.
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Mok, Tsz-kin, and 莫子建. "Modeling, analysis and control design for the UPFC with fuzzy theory and genetic algorithm application." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224969.
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Alshogeathri, Ali Mofleh Ali. "Vehicle-to-Grid (V2G) integration with the power grid using a fuzzy logic controller." Thesis, Kansas State University, 2015. http://hdl.handle.net/2097/20606.
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Master of Science<br>Department of Electrical and Computer Engineering<br>Shelli K. Starrett<br>This thesis introduces a Vehicle to Grid (V2G) system which coordinates the charging, and discharging among the Electric Vehicles (EVs) and two-test systems, to help with peak power shaving and voltage stability of the system. Allowing EVs to charge and discharge without any control may lead to voltage variations and disturbance to the grid, but if the charging and discharging of the EVs is done in a smart manner, they can help the power network. In this thesis, fuzzy logic controllers (FLC) are used to control the flow of power between the grid and the electric vehicles.
The presented work in this thesis mainly focuses on the control architecture for a V2G station that allows for using EVs batteries to help the grid’s voltage stability. The designed controllers sustain the node voltage, and thus also achieve peak shaving. The proposed architectures are tested on 16 -generator and 6-generator test systems to examine the effectiveness of the proposed designs. Five fuzzy logic schemes are tested to illustrate the V2G system’s ability to influence system voltage stability.
The major contributions of this thesis are as follows:
• FLC based control tool for V2G station present at a weak bus in the system.
• Investigate the effect of the station location and voltage sensitivity.
• Comparison of chargers providing real power versus reactive power.
• Simulation of controller and system interactions in a daily load curve cycle.
Keywords: State of Charge (SOC), Electric Vehicle (EV), Fuzzy Logic Controller (FLC), Vehicle to grid (V2G), and Power System Voltage Stability.