Röle geri-beslemeli sistemlerde genetik algoritma ile modelleme
Öz
Röle geri-beslemeli kontrol sistemi kullanarak bir süreci ifade eden transfer fonksiyonun bilinmeyen
parametrelerinin belirlenmesi son zamanlarda oldukça popüler olmuştur. Yüksek dereceli gerçek süreç
transfer fonksiyonları, genellikle, birinci dereceden veya ikinci dereceden kararlı, kararsız ve integratör
içeren model transfer fonksiyonları cinsinden modellenir. Bu tür model transfer fonksiyonlarının röle geribeslemeli
kontrol sistemleri kullanılarak elde edilmesine yönelik literatürde çok sayıda yayın bulunabilir.
Ancak, süreç kontrolünde, bazen süreçler ters cevaplı bir karakteristik gösterebilir. Bu durumda model
transfer fonksiyonu sıfır içerecek şekilde seçilmelidir. Artan model parametre sayısının nedeniyle
literatürdeki yaklaşımlar ile modelleme işleminde bir takım sıkıntılar ortaya çıkmaktadır. Ayrıca, literatürde
ters cevaplı süreçler için, röle geri-beslemeli kontrol sistemi ile modelleme için önerilen çalışma çok azdır.
Bu yüzden, bu bildiri de genetik algoritma ile röle geri-beslemeli kontrol sisteminde ters cevapların
modellenmesi verilecektir. Elde edilen modellerin uygunluğu, gerçek ve model transfer fonksiyonların
frekans cevap karakteristikleri ve sistemin çıkışında elde edilen osilasyonlar karşılaştırılarak denenmiştir.
Anahtar Kelimeler
Modeling in relay feedback systems using genetic algorithm
Abstract
In a control system the controller parameters have
to be chosen so that the system behaves in the
desired way. There are two approaches to find
proper values of the controller parameters. The first
approach is to assume a mathematical model of the
process and then find the controller parameters
based on the assumed model. The second approach
is to choose some controller parameters, observe the
behavior of the feedback loop and modify the
controller parameters until the desired behavior is
achieved.
Model-based controller design is becoming more
popular in engineering research studies. Many
advanced control strategies incorporate various
aspects of the internal model principle, which
requires a model of the system. Some proportionalintegral-derivative
(PID) controllers also include an
implicit process model in their design. For some
controller design approaches, such as a Smith
predictor scheme, a process model is a requirement.
Therefore, being able to obtain an accurate process
model is an important task.
Recently, the relay feedback control (Aström and
Hagglund, 1984) has been widely used for the
identification of an assumed model. The method was
originally proposed for autotuning of a process by
using limit cycle information, Kc and ωc, directly,
but later was also suggested for use in for parameter
estimation of a plant transfer function (Luyben,
1987).
There are several reasons behind the success of the
relay feedback method. First, the relay feedback
method, as normally used, gives important
information about the process frequency response at
the critical gain and frequency, which are the
essential data required for controller design.
Second, the relay feedback method is performed
under closed loop control. If appropriate values of
the relay parameters are chosen, the process may be
kept in the linear region where the frequency
response is of interest. Third, the relay feedback
method eliminates the need for a careful choice of
frequency. Finally, the method is so simple that
operators understand how it works.
In the literature, the use relay feedback method for
open loop stable, unstable and integrating processes
can be found. However, in practice it is possible to
encounter processes with inverse response as well.
There are only a few studies considering the use of
relay feedback control system for identification of
such processes. Also, the numbers of unknown
coefficients in model transfer function of inverse
response processes are increased; hence
identification approaches existing in literature may
become ineffective.
Therefore, to overcome the difficulty in identifying
model parameters of processes with inverse
response, this paper a genetic based identification
method using relay feedback control system for
inverse response processes is given. Obtained model
transfer function and the real process transfer
function frequency response characteristics and
limit cycle oscillations are compared to illustrate the
effectiveness of the proposed identification method.
Keywords
