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Modelagem e controle adaptativo de uma planta did?tica de n?vel com instrumenta??o industrialFonseca, Daniel Guerra Vale da 31 August 2012 (has links)
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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
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Controle de potência do gerador de relutância variável de 12/8 utilizando o controlador fuzzy pi auto-ajustávelCcantuta 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.
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Adaptivní optimální regulátory s principy umělé inteligence v prostředí MATLAB - B&R / Adaptive optimal controllers with principles of artificial intelligenceMrázek, 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.
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Adaptivní regulátory s prvky umělé inteligence / Adaptive Controllers with Elements of Artificial IntelligenceŠulová, 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.
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Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse ControlHakala, 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.
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