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1.3. Objectiveandcontent
perature distributions for different heating setups or workpieces. In
other words, HEPHAISTOS has the potential for systematically alter-
ingheatingpatterns toachievethedesiredheatingratesandtempera-
turedistributionsondifferent typesofheatedworkpieces.
The key to realize this potentiality is to develop an innovative and
effective power control system. By means of advanced modeling and
control methods, the temperature changing trends of different parts
of the workpiece can be predicted and the power allocated to each
heating source can be adjusted accordingly, to guarantee the heating
stabilityandenhance the temperaturehomogeneity.
System Estimation and
Identification
Measure separate temperatures
+ ++
++
+ + +
+++ T1(k)
T2(k)
...
Tn(k)
Value/Action Function
Approximation
Abstract different kinds of information
Tmean(k)
Thigh(k)
Tlow(k)
Phigh(k)
Plow(k)
...
+
Predictive/Adaptive Control
(MPC, NNC)
Intelligent Control
(RLC)
(a)Adaptive temperaturecontrol
System Estimation and
Identification
Measure separate temperatures
+ ++
++
+ + +
+++ T1(k)
T2(k)
...
Tn(k)
Value/Action Function
Approximation
Abstract different kinds of information
Tmean( )
high( )
Tlow( )
high
Plow(k)
...
+
Predictive/Adaptive Control
(MPC, NNC)
Intelligent Control
(RLC)
(b) Intelligent temperaturecontrol
Figure1.2. Diagramsof twodifferent temperaturecontrolapproaches.
Due to the demands of different heating scenarios, two different con-
trol strategies are developed in this dissertation (see figure 1.2). Both
of these two strategies aim to control the heating rate and achieve a
desired temperature distribution, but they control based on different
types of models. In the first adaptive temperature control approach
(figure 1.2a), themodelingisbasedontemperaturesmeasuredatsep-
arate points. During the heating process a system model based on the
temperature-power relation is recursively estimated, and the power
11
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