Global ETD Search

41	Self-Tuning NFC Circuits Li, Yimeng January 2017 (has links) Contactless automatic identification procedures which are called RFID systems (Radio-frequency Identification) have become very popular in recent years for transferring power and data. With the development of RFID technology, the demand of easy transmitting of short data packages has made NFC (Near-field Communication) technology wildly used especially in mobile applications. The communication between a mobile and a tag is achieved through a magnetic field generated by the mobile’s NFC interface. In order to get a maximal power transmission, the tag circuit is designed to operate at the resonance frequency of 13.56 MHz, which is equal to the operation frequency of the mobile’s NFC interface. As mutual inductances provided by different kinds of mobiles exist divergence, optimal power transfer cannot be reached every time. This thesis focuses on the optimization of power transfer during the communications between tags and mobiles with uncertain NFC coils. By incorporating a self-tuning parallel variable capacitance compensation circuitry the resonance frequency of an NFC tag circuit can be self-tuned to 13.56 MHz to ensure an optimal power transmission. This thesis presents both theoretical and experimental analysis of this improved self-tuning NFC circuitry in detail and demonstrates that by digitally tuning a parallel capacitor circuit, the energy transferred to an NFC tag can be optimized when facing different kinds of NFC-enabled mobile phones. Parallel capacitance compensation RFID system NFC Self-tuning NFC Digitally tunable capacitor Annan elektroteknik och elektronik
42	PID tuning with Ant Colony Optimization (ACO) : A framework for a step response based tuning algorithm Björk, Carl Johan January 2018 (has links) The building automation industry lacks an affordable, simple, solution for autonomous PID controller tuning when overhead variables fluctuate. In this project, requested by Jitea AB, a solution was developed, utilising step response process modelling, numerical integration of first order differential equations, and Ant Colony Optimization (ACO). The solution was applied to two control schemes; simulated outlet flow from a virtual water tank, and the physical air pressure in the ventilation system of a preschool in Sweden. An open-loop step response provided the transfer function in each case, which, after some manipulation, could be employed to predict the performance of any given set of PID parameters, based on a weighted cost function. This prediction model was used in ACO to find optimal settings. The program was constructed in both Structured Control Language and Structured Text and documented in an approachable way. The results showed that the program was, in both cases, able to eliminate overshoot and retain the settling time (with a slightly raised rise time) achieved with settings tuned per the current methods of Jitea AB. Noise and oscillations present in the physical system did not appear to have any major negative influence on the tuning process. The program performed above Jitea AB’s expectation, and will be tested in more scenarios, as it showed promise. Autonomous implementation could be of societal benefit through increased efficiency and sustainability in a range of processes. In future studies, focus should be on improving the prediction model, and further optimising the ACO variables. / Byggnadsautomationsbranschen saknar en kostnadseffektiv lösning för att autonomt trimma in PID-regulatorer när överordnade variabler fluktuerar. I detta (av Jitea AB beställda) arbete, utvecklades en lösning baserad på stegsvarsmodellering, numerisk integration av första gradens ordinära differentialekvationer och myrkolonisoptimering (ACO). Lösningen applicerades i två regleringsfall; en simulerad utloppsventil från en virtuell vattentank, och det fysiska lufttrycket i ventilationssystemet på en förskola i Sverige. Ett stegsvar med öppen slinga gav en överföringsfunktion i respektive fall, som efter viss manipulering kunde nyttjas för att förutspå prestandan för en uppsättning PID-parametrar baserat på en samlad, viktad kostnadsfunktion. Predikteringsmodellen implementerades i ACO för att finna optimala parametrar. Programmet konstruerades i Structured Control Language och Structured Text, och dokumenterades på ett pedagogiskt sätt. Resultaten visade att programmet (i båda fallen) klarade att eliminera översläng med bibehållen stabiliseringstid (och något förskjuten stigningstid) jämfört med Jitea AB:s existerande trimningsmetod. Signalbrus och oscillationer i det fysiska systemet verkade inte ha någon avsevärd negativ inverkan på trimningsprocessen. Programmet presterade över Jitea AB:s förväntan, och kommer (med tanke på de lovande resultaten) fortsatt att testas i fler scenarion. Implementation av en autonom version skulle kunna innebära flera samhälleliga förmåner i form av ökad verkningsgrad och hållbarhet i en rad processer. I framtida studier bör fokus läggas på att ytterligare förbättra prediktionsmodellen, samt att vidare utforska de optimala myrkolonisvariablerna. PID self-tuning control system building automation ant colony optimization neural networks open-loop closed-loop step response. PID självtrimning reglersystem byggnadsautomation myrkolonioptimering neurala nätverk öppen slinga sluten slinga stegsvar. Annan elektroteknik och elektronik
43	Modelagem e controle adaptativo de uma planta did?tica de n?vel com instrumenta??o industrial Fonseca, Daniel Guerra Vale da 31 August 2012 (has links) Made available in DSpace on 2014-12-17T14:56:07Z (GMT). No. of bitstreams: 1 DanielGVF_DISSERT.pdf: 2881772 bytes, checksum: 5236953fb6bb70560393eeeaa01f96f9 (MD5) Previous issue date: 2012-08-31 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The control, automation and optimization areas help to improve the processes used by industry. They contribute to a fast production line, improving the products quality and reducing the manufacturing costs. Didatic plants are good tools for research in these areas, providing a direct contact with some industrial equipaments. Given these capabilities, the main goal of this work is to model and control a didactic plant, which is a level and flow process control system with an industrial instrumentation. With a model it is possible to build a simulator for the plant that allows studies about its behaviour, without any of the real processes operational costs, like experiments with controllers. They can be tested several times before its application in a real process. Among the several types of controllers, it was used adaptive controllers, mainly the Direct Self-Tuning Regulators (DSTR) with Integral Action and the Gain Scheduling (GS). The DSTR was based on Pole-Placement design and use the Recursive Least Square to calculate the controller parameters. The characteristics of an adaptive system was very worth to guarantee a good performance when the controller was applied to the plant / As ?reas de controle, automa??o e otimiza??o contribuem para a melhoria dos processos utilizados pelas ind?strias, permitindo uma linha de produ??o r?pida, aprimorando a qualidade do produto final e reduzindo os custos de produ??o. Boas ferramentas para o desenvolvimento de pesquisas nestas ?reas s?o as plantas did?ticas, pois proporcionam um contato direto com equipamentos semelhantes ou at? mesmo usados no setor industrial. Em vista dessas capacidades, o objetivo deste trabalho ? modelar e controlar uma planta did?tica que consiste de um sistema de controle de processo para vaz?o e n?vel com instrumenta??o industrial. Com o modelo ? poss?vel construir um simulador capaz de permitir estudos a respeito do funcionamento do sistema, sem os gastos com a opera??o do processo real. ? o caso de experimentos com controladores, que podem ser testados diversas vezes antes de serem efetivamente utilizados no processo real. Dentre os diversos tipos de controladores existentes, foi dado foco aos de tipo adaptativo, principalmente ao auto-sintoniz?vel direto (Direct Self-Tuning Regulator DSTR) com a??o integral e ao controlador com Escalonamento de Ganho (Gain Scheduling GS). O controlador DSTR foi projetado com base no m?todo de posicionamento de p?los e teve seus par?metros calculados atrav?s da t?cnica dos m?nimos quadrados recursivos. As caracter?sticas dos sistemas adaptativos foram de grande valia para garantir um desempenho satisfat?rio dos controladores, quando aplicados ? planta CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
44	Controle de potência do gerador de relutância variável de 12/8 utilizando o controlador fuzzy pi auto-ajustável Ccantuta Chirapo, Karlos Alexander January 2018 (has links) Orientador: Prof. Dr. José Luis Azcue Puma / Coorientador: Prof. Dr. José Alberto Torrico Altuna / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, Santo André, 2018. / Este trabalho apresenta o controle de potência do gerador de relutância variável de 12/8 utilizando o controlador Fuzzy PI auto-ajustável e o controlador P+ressonante para o conversor conectado à rede elétrica. Inicialmente são estudados os princípios de operação da máquina e a topologia do conversor eletrônico de potência. Usando o software de simulação Matlab/Simulink é implementado o modelo da máquina de relutância variável utilizando as curvas características de corrente e de torque. Com o objetivo de controlar a potência gerada pela máquina e utilizando seu modelo matemático dinâmico projeta-se o controlador PI, e implementa-se o controlador Fuzzy PI auto-ajustável para atingir o mesmo objetivo. Assim, projeta-se também um controlador PI para o controle da tensão CC além de um controlador P+ressonante com o objetivo de melhorar a resposta em regime permanente da potência injetada na rede elétrica. Para este propósito é utilizado um conversor monofásico de dois níveis. Os resultados das simulações mostram o bom desempenho do sistema proposto e o controlador de potência é validado também através de resultados experimentais. / This work presents the power control of the 12/8 variable reluctance generator using Fuzzy self-tuning PI controller and the P+resonant controller for the converter connected to the electric network. Initially are studied the principles of operation of the machine and power electronic converter topology. Using the Matlab/Simulink simulation software is implemented the model of switched reluctance machine using the current and its torque characteristic curves. With the purpose of controlling the power generated by the machine and using his dynamic mathematical model is designed the PI controller, and the Fuzzy self-tuning PI controller is implemented to achieve the same objective. So, also is designed a PI controller for control of the link DC, in addition to the P+resonant controller with the objective of improving the response to its steady state of the injected power into the electric network. For this purpose it is used a single-phase two-level converter. The results of the simulations show the good performance of the proposed system and the power controller is validated by experimental results. MRV MÁQUINA DE RELUTÂNCIA VARIÁVEL GERADOR DE RELUTÂNCIA VARIÁVEL CONTROLADOR FUZZY PI AUTO-AJUSTÁVEL CONTROLADOR P+RESSONANTE SRM SWITCHED RELUCTANCE MACHINE SWITCHED RELUCTANCE GENERATOR SELF-TUNING FUZZY PI CONTROLLER P+RESONANT CONTROLLER
45	Adaptivní optimální regulátory s principy umělé inteligence v prostředí MATLAB - B&R / Adaptive optimal controllers with principles of artificial intelligence Mrázek, Michal January 2008 (has links) Master’s thesis describes adaptive optimal controller design which change parameters of algorithm based on the system information regard for optimal criterion. Generally, the optimal controller solves the problem of minimum states vector. Problems of desired value and steady-state error are solved by variation in optimization algorithm.
46	Adaptivní regulátory s prvky umělé inteligence / Adaptive Controllers with Elements of Artificial Intelligence Šulová, Markéta January 2009 (has links) The aim of the thesis is to improve the control quality of the adaptive systems (Self Tuning Controllers). The thesis mainly deals with problematical identification part of the adaptive system. This part demonstrates a weak point for existing adaptive systems. Paradoxically, the quality of the adaptive system depends mainly on the identification part because on the basis of the process model obtained by identification are worked out parameters of a control part, afterwards the control action plan is established. Knowledge of the modern control methods is used and a new identification algorithm for closed loop identification is proposed. This simple, fast and efficient algorithm overcomes all disadvantages of current classical identification methods based on least mean-square algorithms. The possibility of the choice of a short sample time, one tuning parameter ability to adjust the control process, the ability to identify processes in real use belong to its main goals. This algorithm was built in the adaptive system and then it was tested on a set of simulation and real models with surprisingly excellent results. The successful implementation of the algorithm into the programmable logic controller was also realized. One part of the thesis introduces a new universal graphics environment for testing and verifying control algorithms.
47	Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control Hakala, Tim 31 January 2006 (has links) (PDF) A new method of adaptive impulse control is developed to precisely and quickly control the position of machine components subject to friction. Friction dominates the forces affecting fine positioning dynamics. Friction can depend on payload, velocity, step size, path, initial position, temperature, and other variables. Control problems such as steady-state error and limit cycles often arise when applying conventional control techniques to the position control problem. Studies in the last few decades have shown that impulsive control can produce repeatable displacements as small as ten nanometers without limit cycles or steady-state error in machines subject to dry sliding friction. These displacements are achieved through the application of short duration, high intensity pulses. The relationship between pulse duration and displacement is seldom a simple function. The most dependable practical methods for control are self-tuning; they learn from online experience by adapting an internal control parameter until precise position control is achieved. To date, the best known adaptive pulse control methods adapt a single control parameter. While effective, the single parameter methods suffer from sub-optimal settling times and poor parameter convergence. To improve performance while maintaining the capacity for ultimate precision, a new control method referred to as Adaptive Impulse Control (AIC) has been developed. To better fit the nonlinear relationship between pulses and displacements, AIC adaptively tunes a set of parameters. Each parameter affects a different range of displacements. Online updates depend on the residual control error following each pulse, an estimate of pulse sensitivity, and a learning gain. After an update is calculated, it is distributed among the parameters that were used to calculate the most recent pulse. As the stored relationship converges to the actual relationship of the machine, pulses become more accurate and fewer pulses are needed to reach each desired destination. When fewer pulses are needed, settling time improves and efficiency increases. AIC is experimentally compared to conventional PID control and other adaptive pulse control methods on a rotary system with a position measurement resolution of 16000 encoder counts per revolution of the load wheel. The friction in the test system is nonlinear and irregular with a position dependent break-away torque that varies by a factor of more than 1.8 to 1. AIC is shown to improve settling times by as much as a factor of two when compared to other adaptive pulse control methods while maintaining precise control tolerances. control position adaptive impulsive settling-time nonlinear friction pulses displacements precise tolerances log-spaced update distributed learning Coulomb Stribeck Tomizuka Yang AIC PID MRAC STR RTAI Linux FreeBSD kernel modules microcontroller convergence practical self-tuning methods techniques limit-cycles steady-state error zero stable stability bound envelope partitioned scheme lookup-table multi-point adaptation repeatable mean servo motor exponential square-law rise-time real-time log-log interpolation pro-forma curve-fit sensitivity compliance variable static dynamic response torque acceleration velocity optical encoder parameters evolution fixed-law enhanced split weighting initialization trajectory layered processes Mechanical Engineering

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