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5. ExperimentalResults
5.25b). Theproblemiscausedbytheerrorfromeitherthesystemiden-
tification or the GA controller. The high control input dimension of
HEPHAISTOS is a large computation burden for both of them, espe-
cially for the GA algorithm. The overall searching space in the GA
algorithm increases exponentially with the input dimension. When
all other parameters (such as the number of generations used and the
numberoftheindividuals ineachgeneration)arefixed,ahigherinput
dimensionleadstoamuchlargersearchingspaceintheGAalgorithm,
which correspondingly lowers the probability that a good control so-
lution can be obtained. In practical experiments, the most simple and
effective way to reduce the temperature overshoot and enhance the
entirecontrolperformance is touse lessheatingsources.
For example, in the experiment shown by figure 5.28, all heating
sources below the metal table are not used during the heating pro-
cess (in each module only sources No. 2, No. 4, No. 10 and No. 11
are switched on, see figure 2.13). Overall 8 heating sources are used
for the controlling. On the one hand, the computation burdens for
both the system estimator and the controller are largely reduced. On
the other hand, the total 8 sourcesstill guarantee a huge control space
and control diversity. With fewer heating sources, the accuracies of
thesystemidentificationandtheGAcontrollerbothcanbeimproved.
Hence, from the results in figure 5.28, it is clear that both the temper-
ature overshoot and the final temperature window are significantly
reducedusing lesssources.
As aforementioned in the system modeling part (chapter 2) and the
beginning of this section, thermal conduction plays an important rule
in the model derivation as well as controlling process. In order to fur-
ther testhowdifferentconductioneffects influence thecontrolperfor-
mance, a different setup was implemented in our experiments, such
as in figure 5.29. This setup has the same aluminum plate, vacuum
bagging and other components, except one big difference. Instead of
one big silicone rubber workpiece, in the second setup 5.29 there are
five independent small workpieces. Compared with the first setup
5.21,apparentlythesecondsetuphasamuchlowerinfluencefromthe
thermal conduction and convection parts. In other words, all cooling
effects includingthethermalconvection,radiationandconductionfor
the five workpieces are almost the same. Therefore in such cases, the
propertiesofeachcontrolmethodcanbemoregreatlyreflected.
168
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
- Titel
- Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
- Autor
- Yiming Sun
- Verlag
- KIT Scientific Publishing
- Ort
- Karlsruhe
- Datum
- 2016
- Sprache
- englisch
- Lizenz
- CC BY-SA 3.0
- ISBN
- 978-3-7315-0467-2
- Abmessungen
- 14.8 x 21.0 cm
- Seiten
- 260
- Schlagwörter
- Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
- Kategorie
- Technik