20.500.12556/RUL-105879
SAMORAZVIJAJOČI SE SISTEMI PRI SPREMLJANJU IN VODENJU PROCESOV
EVOLVING SYSTEMS IN PROCESS MONITORING AND CONTROL
V pričujoči doktorski disertaciji smo raziskovali različna področja uporabe
samorazvijajočih se sistemov, kot so vodenje, identikacija in spremljanje procesov.
Samorazvijajoči se sistemi, ki jih obravnavamo v disertaciji, temeljijo na
posplošenem mehkem modelu AnYa (po priimkih avtorjev Angelov in Yager), ki
se razlikuje od klasičnih mehkih modelov (Mamdani in Takagi-Sugeno) v načinu
deniranja strukture modela. Namesto vnaprej deniranih mehkih pravil (Gaussovo,
trikotno, lingvistično itn.), model AnYa na podlagi sprotno sprejetih podatkov
tvori mehka pravila v obliki oblakov podatkov (ang. data clouds). Z vsakim
novim prejetim podatkom se struktura in parametri modela prilagajajo novim
spremenjenim stanjem v procesu. To nam omogoča implementacijo različnih algoritmov
sprotnega vodenja, identikacije ali spremljanja dinamičnih procesov.
Poleg implementacije teh algoritmov smo v doktorski disertaciji obravnavali tudi
mehanizme samorazvijanja modelov za dodajanje novih in odstranjevanje nepomembnih
oblakov. Prav tako smo iskali načine, kako preprečiti dodajanje oblakov
na osnovi osamelcev.
V disertaciji smo predstavili robusten samorazvijajoči se adaptivni mehki regulator
(ang. robust evolving cloud-based controller, RECCo). Regulator je sestavljen
iz dveh glavnih delov: samorazvijajoča se struktura modela (z mehanizmom
za dodajanje novih oblakov na podlagi lokalne gostote podatkov) in sprotna adaptacija
parametrov lokalnih regulatorjev (na podlagi gradienta kriterijske funkcije).
Mehanizem samorazvijanja modela skrbi za zaznavanje nelinearnih področij v
procesu, kar pomeni, da se parametri lokalnih regulatorjev prilagajajo delovni
točki procesa. Z normiranjem podatkovnega prostora smo dosegli enostavnejše
nastavljanje začetnih parametrov regulatorja. Podali smo tudi smernice, kako
nastaviti oziroma izračunati začetne vrednosti parametrov regulatorja. V doktorski
disertaciji smo prikazali nekaj primerov uporabe RECCo-regulatorja na
simuliranih in realnih napravah. Vodenje na simuliranih procesih smo izvedli na
modelu toplotnega izmenjevalnika in na modelu distribuiranega sistema sončnih
kolektorjev. Uporaba vodenja na realnih napravah pa je bila izvedena pri regulaciji
temperature na toplotnem izmenjevalniku in regulaciji nivoja na sistemu
dveh povezanih tankov.
Mehanizem samorazvijanja na osnovi oblakov podatkov smo vpeljali tudi v
mehki prediktivno funkcijski regulator (ang. fuzzy cloud-based predictive func-
tional controller, FCPFC). Za delovanje tega regulatorja potrebujemo model
procesa, ki ga želimo voditi. S tem namenom smo združili samorazvijajoči se
model z rekurzivno metodo najmanjših kvadratov. Na ta način lahko identiciramo
dinamičen model procesa, ki je potem del prediktivnega regulatorja. Model
uporabimo za predikcijo reguliranega signala na vnaprej določenem horizontu in
nato določimo še regulirni signal, ki minimizira razliko med izhodnim in referen
čnim/želenim signalom. Taksen pristop je primeren za regulacijo nelinearnih
dinamičnih procesov. Delovanje predlaganega prediktivnega regulatorja FCPFC
smo preizkusili na modelu reaktorja z neprekinjenim mešanjem (ang. continu-
ous stirred tank reaktor, CSTR). Dobljene rezultate smo primerjali z RECCoregulatorjem.
V nadaljevanju smo predlagali in preizkusili samorazvijajoči se model na
osnovi oblakov za identikacijo dinamičnih sistemov. V tem primeru smo raziskali
različne mehanizme dodajanja in odstranjevanja oblakov in njihov vpliv
na učinkovitost celotne metode. Predlagano metodo smo preizkusili na dveh
različnih primerih. Prvi primer je model kemičnega procesa Tennessee Eastman,
ki ima zelo kompleksno strukturo in dinamiko. Iz tega modela smo pridobili
simulirane podatke ter poskušali pridobiti modele kazalnikov proizvodnje
učinkovitosti. Rezultate smo primerjali z metodo eFuMo ter z nevronskimi
mrežami. Drugi primer je bil realen sistem hladilne postaje, ki obratuje v enem
od podjetij v Sloveniji. Pridobljene podatke smo prav tako uporabili za identi-
kacijo dinamičnih modelov nekaj ključnih kazalnikov proizvodnje. Te modele smo
naknadno uporabili za nadzorovano in prediktivno preklapljanje hladilnih agregatov,
ki so ključni elementi celotnega sistema. Izkazalo se je, da z uporabo modelov
lahko preprečimo nepotrebna preklapljanja agregatov in s tem omogočimo boljšo
učinkovitost celotnega sistema.
V zadnjem delu smo samorazvijajoči se model uporabili kot orodje za spremljanje
procesov. Z uporabo mehanizmov za dodajanje novih oblakov lahko
deniramo področje procesa, ki opisuje normalno stanje delovanja, in področje,
ki označuje napako na procesu. Nato lahko z izračunom lokalnih gostot za vsak
podatek posebej določimo ali predstavlja napako oziroma normalno delovanje.
Predlagali smo tudi izračun delnih lokalnih gostot z upoštevanjem najbolj vplivnih
komponent. Delovanje metode smo preizkusili na področju zaznavanja napak
na sistemu Tennessee Eastman. Rezultate smo primerjali z nekaj znanimi
metodami za zaznavanje napak na procesih, kot so PCA (ang. principal compo-
nent analysis), ICA (ang. independent component analysis) in FDA (ang. sher
discriminate analysis). Rezultati metode so primerljivi in dosegajo podobno natan
čnost, kot že uveljavljene metode na tem področju.
Na koncu smo samorazvijajoči se model razdelili na več hierarhičnih nivojev
z namenom zaznavanja manevrov pri voznikih osebnih avtomobilov, kot so prehitevanje,
zaviranje, ustavljanje in podobno. Metoda uporablja le osnovne senzorje
(in ne naprednih, kot so kamere, laserji itd.), ki so del standardne opreme osebnih
avtomobilov. Izkazalo se je, da predlagani hierarhični koncept od spodaj
navzgor (od manj do bolj kompleksnih akcij) v kombinaciji s samorazvijajočim
se modelom uspešno zaznava in ločuje med različnimi manevri pri voznikih.
In the doctoral thesis the usage of evolving systems on dierent elds was
investigated, namely the eld of control, system identication and process monitoring.
The evolving systems in the current thesis are based on the simplied
fuzzy model AnYa which diers from the classical ones (such as Mamdani and
Takagi-Sugeno) in the way how the model structure is dened. The AnYa model
uses data clouds in the antecedent part of the fuzzy system instead of predened
fuzzy rules (Gaussian, triangular, trapezoid etc.) in the classical models. Therefore,
the structure of the AnYa could be adapted with each data point received in
online manner. This leads to easy implementation of advanced on-line methods
for control, identication or monitoring of dynamic processes. Beside this we
have investigated dierent evolving mechanisms for adding new rules/clouds and
for removing less important ones.
Firstly we have presented a robust evolving cloud-based controller RECCo for
nonlinear processes. The controller consists of two main parts: evolving mechanism
(adding new clouds according to the local density of the data) and adaptive
law of the local controllers' parameters (based on gradient descent method). The
evolving part takes care of the controlled process nonlinearity and the adaptive
law adjusts to the current operating point. We proposed a normalization of the
data space which simplies the process of setting the initial parameters. Also
some instructions for setting/calculating the initial parameters are given. In the
dissertation we show some practical examples of using the controller on simulated
and real processes. For the simulated examples we used a model of heat
exchanger and a model of distributed solar collector eld. Moreover a real plant
of heat exchanger and two tank plant were used for temperature and level control,
respectively.
Based on the evolving model we also introduce a fuzzy cloud-based predictive
functional controller (FCPFC). This controller requires a model of the controlled
process. With this purpose we joined the evolving mechanisms with the recursive
weighted least square method. Actually we developed a tool for identication of
dynamic process model. The model is further used for the controlled signal prediction
in a certain horizon and then the control signal is chosen to minimize the
error between the predicted and the desired value of the signal. This approach
is suitable for controlling nonlinear dynamic processes. The eciency of the proposed
controller FCPFC was tested on Continuous Stirred Tank Reactor, CSTR.
The results were also compared with the RECCo controller.
In the second part of the doctoral thesis we proposed an evolving method
for dynamic process identication. Actually the recursive identication method
was already tackled in the previous part when FCPFC controller was proposed.
In this part we additionally investigated dierent mechanisms for adding
and removing data clouds (fuzzy rules) and their impact on the overall identi
cation method eciency. The proposed method was tested on two dierent
examples. The rst example was Tennessee Eastman process which has really
complex structure with dynamic behavior. We acquired the necessary data from
the model and then we identied the models of the production Performance Indicators
(pPI). The obtained results were compared to the established methods
eFuMo and neural networks. The second example was a real process of water
chiller plant (WCP). The data were acquired directly from the plant and were
used for model identication of the key indicators. The models were further used
for process monitoring and predictive operating with the chillers, which are the
key elements of the whole system. It has been shown that using the acquired
models we can prevent unnecessary switching of the chillers which leads to more
ecient operation of the system.
In the last part of the thesis we investigated the usage of the evolving system
for process monitoring and fault detection purposes. Using the evolving model
we can dene the part of data space which describe normal process operation
and faults. According to the local density we can easily determine if each data
sample is a fault or not. Moreover we proposed a partial data density calculation
which takes into account only the most in
uential components. The eciency
of the method was tested on the Tennessee Eastman process. The results were
compared with well established methods on the eld such as: PCA (principal
component analysis), ICA (independent component analysis) and FDA (sher
discriminate analysis). With the comparison analysis of the results we show that
the proposed evolving method is comparable with the established ones.
At the end we developed a hierarchical evolving model for car-driver behavior
detection. The method uses the basic sensors (no cameras, radars etc.) in the
car which are part of standard equipment of a modern car. We show that with
the proposed hierarchical concept and the evolving model we can eciently detect
dierent maneuvers (such as overtaking, stopping, breaking, etc.) and can
dierentiate between them.
model AnYa
samorazvijajoči se adaptivni mehki regulator
AnYa model
robust evolving cloud-based controller
true
false
false
Slovenski jezik
Angleški jezik
Doktorsko delo/naloga
2018-12-21 14:40:02
2018-12-21 14:40:12
2022-08-18 03:46:35
0000-00-00 00:00:00
2018
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1970-01-01
45143
Andonovski_Goran_-_SAMORAZVIJAJOCI_SE_SISTEMI_PRI_SPREMLJANJU_IN_VODENJU_PROCESOV.pdf
Andonovski_Goran_-_SAMORAZVIJAJOCI_SE_SISTEMI_PRI_SPREMLJANJU_IN_VODENJU_PROCESOV.pdf
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https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=116574
Fakulteta za elektrotehniko
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